Rolv-Arild
commited on
Commit
·
5a2b856
1
Parent(s):
05ba5dd
Training in progress, step 500
Browse files- .gitattributes +1 -0
- .gitignore +1 -0
- added_tokens.json +1 -0
- config.json +107 -0
- eval.py +175 -0
- preprocessor_config.json +9 -0
- pytorch_model.bin +3 -0
- run.sh +39 -0
- run_speech_recognition_ctc.py +819 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- training_args.bin +3 -0
- vocab.json +1 -0
- wandb/debug-internal.log +1 -0
- wandb/debug.log +1 -0
- wandb/latest-run +1 -0
- wandb/run-20220523_091609-1iboydmy/files/config.yaml +0 -0
- wandb/run-20220523_091609-1iboydmy/files/output.log +1379 -0
- wandb/run-20220523_091609-1iboydmy/files/requirements.txt +77 -0
- wandb/run-20220523_091609-1iboydmy/files/wandb-metadata.json +62 -0
- wandb/run-20220523_091609-1iboydmy/files/wandb-summary.json +0 -0
- wandb/run-20220523_091609-1iboydmy/logs/debug-internal.log +0 -0
- wandb/run-20220523_091609-1iboydmy/logs/debug.log +189 -0
- wandb/run-20220523_091609-1iboydmy/run-1iboydmy.wandb +3 -0
- wandb/run-20220523_103002-wygrs7tw/files/config.yaml +0 -0
- wandb/run-20220523_103002-wygrs7tw/files/output.log +1746 -0
- wandb/run-20220523_103002-wygrs7tw/files/requirements.txt +77 -0
- wandb/run-20220523_103002-wygrs7tw/files/wandb-metadata.json +62 -0
- wandb/run-20220523_103002-wygrs7tw/files/wandb-summary.json +0 -0
- wandb/run-20220523_103002-wygrs7tw/logs/debug-internal.log +0 -0
- wandb/run-20220523_103002-wygrs7tw/logs/debug.log +181 -0
- wandb/run-20220523_103002-wygrs7tw/run-wygrs7tw.wandb +3 -0
- wandb/run-20220523_115145-3dybzmyz/files/config.yaml +0 -0
- wandb/run-20220523_115145-3dybzmyz/files/output.log +1788 -0
- wandb/run-20220523_115145-3dybzmyz/files/requirements.txt +77 -0
- wandb/run-20220523_115145-3dybzmyz/files/wandb-metadata.json +62 -0
- wandb/run-20220523_115145-3dybzmyz/files/wandb-summary.json +0 -0
- wandb/run-20220523_115145-3dybzmyz/logs/debug-internal.log +0 -0
- wandb/run-20220523_115145-3dybzmyz/logs/debug.log +27 -0
- wandb/run-20220523_115145-3dybzmyz/run-3dybzmyz.wandb +3 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.wandb filter=lfs diff=lfs merge=lfs -text
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.gitignore
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checkpoint-*/
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added_tokens.json
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{"<s>": 32, "</s>": 33}
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config.json
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{
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"_name_or_path": "facebook/wav2vec2-xls-r-1b",
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"activation_dropout": 0.055,
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"adapter_kernel_size": 3,
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"adapter_stride": 2,
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"add_adapter": false,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForCTC"
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],
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"attention_dropout": 0.094,
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"bos_token_id": 1,
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"classifier_proj_size": 256,
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"codevector_dim": 1024,
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"contrastive_logits_temperature": 0.1,
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"conv_bias": true,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2
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],
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": true,
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"diversity_loss_weight": 0.1,
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"do_stable_layer_norm": true,
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.04,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"hidden_act": "gelu",
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"hidden_dropout": 0.047,
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"hidden_size": 1280,
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"initializer_range": 0.02,
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"intermediate_size": 5120,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.041,
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"mask_feature_length": 64,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.25,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.082,
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"model_type": "wav2vec2",
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"num_adapter_layers": 3,
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"num_attention_heads": 16,
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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 48,
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"num_negatives": 100,
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"output_hidden_size": 1280,
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"pad_token_id": 31,
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"proj_codevector_dim": 1024,
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"tdnn_dilation": [
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1,
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2,
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3,
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1,
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1
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],
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"tdnn_dim": [
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512,
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512,
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512,
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512,
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1500
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],
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"tdnn_kernel": [
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5,
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],
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"torch_dtype": "float32",
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"transformers_version": "4.18.0",
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"use_weighted_layer_sum": false,
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"vocab_size": 34,
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"xvector_output_dim": 512
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}
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eval.py
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#!/usr/bin/env python3
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import argparse
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import re
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from typing import Dict
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import torch
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from datasets import Audio, Dataset, load_dataset, load_metric
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from transformers import AutoFeatureExtractor, pipeline, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2FeatureExtractor
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from pyctcdecode import BeamSearchDecoderCTC
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def log_results(result: Dataset, args: Dict[str, str]):
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"""DO NOT CHANGE. This function computes and logs the result metrics."""
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log_outputs = args.log_outputs
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lm = "withLM" if args.use_lm else "noLM"
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model_id = args.model_id.replace("/", "_")
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dataset_id = "_".join(args.dataset.split("/") + [model_id, args.config, args.split, lm])
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# load metric
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wer = load_metric("wer")
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cer = load_metric("cer")
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# compute metrics
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wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
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cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
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# print & log results
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result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
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print(result_str)
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with open(f"{dataset_id}_eval_results.txt", "w") as f:
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f.write(result_str)
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# log all results in text file. Possibly interesting for analysis
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if log_outputs is not None:
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pred_file = f"log_{dataset_id}_predictions.txt"
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target_file = f"log_{dataset_id}_targets.txt"
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with open(pred_file, "w") as p, open(target_file, "w") as t:
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# mapping function to write output
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def write_to_file(batch, i):
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p.write(f"{i}" + "\n")
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p.write(batch["prediction"] + "\n")
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t.write(f"{i}" + "\n")
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t.write(batch["target"] + "\n")
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result.map(write_to_file, with_indices=True)
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def normalize_text(text: str, dataset: str) -> str:
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"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\–\_\\\+\#\/]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
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text = re.sub(chars_to_ignore_regex, "", text.lower()) + " "
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if dataset.lower().endswith("nst"):
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text = text.lower()
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text = text.replace("(...Vær stille under dette opptaket...)", "")
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text = re.sub('[áàâ]', 'a', text)
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text = re.sub('[ä]', 'æ', text)
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text = re.sub('[éèëê]', 'e', text)
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text = re.sub('[íìïî]', 'i', text)
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text = re.sub('[óòöô]', 'o', text)
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text = re.sub('[ö]', 'ø', text)
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text = re.sub('[ç]', 'c', text)
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text = re.sub('[úùüû]', 'u', text)
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# text = re.sub('\\(?=(Punktum|Komma|Utropstegn|Spørsmålstegn))', ' ', text)
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text = re.sub('\s+', ' ', text)
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elif dataset.lower().endswith("npsc"):
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text = re.sub('[áàâ]', 'a', text)
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text = re.sub('[ä]', 'æ', text)
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text = re.sub('[éèëê]', 'e', text)
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text = re.sub('[íìïî]', 'i', text)
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text = re.sub('[óòöô]', 'o', text)
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text = re.sub('[ö]', 'ø', text)
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text = re.sub('[ç]', 'c', text)
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text = re.sub('[úùüû]', 'u', text)
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text = re.sub('\s', ' ', text)
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text = re.sub('<ee>', 'eee', text)
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text = re.sub('<qq>', 'qqq', text)
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text = re.sub('<mm>', 'mmm', text)
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text = re.sub('<inaudible>', 'xxx', text)
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# # In addition, we can normalize the target text, e.g. removing new lines characters etc...
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# # note that order is important here!
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# token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
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# for t in token_sequences_to_ignore:
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# text = " ".join(text.split(t))
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return text
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def main(args):
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# load dataset
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dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
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# for testing: only process the first two examples as a test
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# dataset = dataset.select(range(10))
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# load processor
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feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
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sampling_rate = feature_extractor.sampling_rate
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# resample audio
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dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
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# load eval pipeline
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if args.device is None:
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args.device = 0 if torch.cuda.is_available() else -1
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# asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
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feature_extractor_dict, _ = Wav2Vec2FeatureExtractor.get_feature_extractor_dict(args.model_id)
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feature_extractor_dict["processor_class"] = "Wav2Vec2Processor" if not args.use_lm else "Wav2Vec2ProcessorWithLM"
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feature_extractor = Wav2Vec2FeatureExtractor.from_dict(feature_extractor_dict)
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asr = pipeline("automatic-speech-recognition", model=args.model_id, feature_extractor=feature_extractor, device=args.device, decoder=BeamSearchDecoderCTC.load_from_dir("./"))
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# map function to decode audio
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def map_to_pred(batch):
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prediction = asr(
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batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
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)
|
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batch["prediction"] = prediction["text"]
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batch["target"] = normalize_text(batch["text"], args.dataset)
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return batch
|
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|
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# run inference on all examples
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132 |
+
result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
|
133 |
+
|
134 |
+
# compute and log_results
|
135 |
+
# do not change function below
|
136 |
+
log_results(result, args)
|
137 |
+
|
138 |
+
|
139 |
+
if __name__ == "__main__":
|
140 |
+
parser = argparse.ArgumentParser()
|
141 |
+
|
142 |
+
parser.add_argument(
|
143 |
+
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
|
144 |
+
)
|
145 |
+
parser.add_argument(
|
146 |
+
"--dataset",
|
147 |
+
type=str,
|
148 |
+
required=True,
|
149 |
+
help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
|
150 |
+
)
|
151 |
+
parser.add_argument(
|
152 |
+
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
|
153 |
+
)
|
154 |
+
parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
|
155 |
+
parser.add_argument(
|
156 |
+
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
|
157 |
+
)
|
158 |
+
parser.add_argument(
|
159 |
+
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
|
160 |
+
)
|
161 |
+
parser.add_argument(
|
162 |
+
"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
|
163 |
+
)
|
164 |
+
parser.add_argument(
|
165 |
+
"--device",
|
166 |
+
type=int,
|
167 |
+
default=None,
|
168 |
+
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
|
169 |
+
)
|
170 |
+
parser.add_argument(
|
171 |
+
"--use_lm", action="store_true", help="If defined, use included language model as the decoder."
|
172 |
+
)
|
173 |
+
args = parser.parse_args()
|
174 |
+
|
175 |
+
main(args)
|
preprocessor_config.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"do_normalize": true,
|
3 |
+
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
|
4 |
+
"feature_size": 1,
|
5 |
+
"padding_side": "right",
|
6 |
+
"padding_value": 0,
|
7 |
+
"return_attention_mask": true,
|
8 |
+
"sampling_rate": 16000
|
9 |
+
}
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bb89a06605f56711cff18daf05feca605389bdcb2628e4715f228b7f66767dd5
|
3 |
+
size 3850439217
|
run.sh
ADDED
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
WANDB_ENTITY=NbAiLab WANDB_PROJECT=wav2vec2 python run_speech_recognition_ctc.py \
|
2 |
+
--model_name_or_path="facebook/wav2vec2-xls-r-1b" \
|
3 |
+
--hub_model_id="NbAiLab/wav2vec2-1b-npsc-nst-bokmaal" \
|
4 |
+
--output_dir="./" \
|
5 |
+
--overwrite_output_dir \
|
6 |
+
--num_train_epochs="40" \
|
7 |
+
--per_device_train_batch_size="12" \
|
8 |
+
--per_device_eval_batch_size="12" \
|
9 |
+
--gradient_accumulation_steps="2" \
|
10 |
+
--learning_rate="2e-5" \
|
11 |
+
--warmup_steps="2000" \
|
12 |
+
--length_column_name="input_length" \
|
13 |
+
--evaluation_strategy="steps" \
|
14 |
+
--text_column_name="text" \
|
15 |
+
--save_steps="500" \
|
16 |
+
--eval_steps="500" \
|
17 |
+
--logging_steps="100" \
|
18 |
+
--layerdrop="0.041" \
|
19 |
+
--attention_dropout="0.094" \
|
20 |
+
--activation_dropout="0.055" \
|
21 |
+
--hidden_dropout="0.047" \
|
22 |
+
--save_total_limit="3" \
|
23 |
+
--freeze_feature_encoder \
|
24 |
+
--feat_proj_dropout="0.04" \
|
25 |
+
--mask_time_prob="0.082" \
|
26 |
+
--mask_time_length="10" \
|
27 |
+
--mask_feature_prob="0.25" \
|
28 |
+
--mask_feature_length="64" \
|
29 |
+
--gradient_checkpointing \
|
30 |
+
--min_duration_in_seconds="0.5" \
|
31 |
+
--max_duration_in_seconds="30.0" \
|
32 |
+
--use_auth_token \
|
33 |
+
--seed="42" \
|
34 |
+
--fp16 \
|
35 |
+
--group_by_length \
|
36 |
+
--do_train --do_eval \
|
37 |
+
--push_to_hub \
|
38 |
+
--preprocessing_num_workers="32" \
|
39 |
+
--ctc_zero_infinity
|
run_speech_recognition_ctc.py
ADDED
@@ -0,0 +1,819 @@
|
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|
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|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
# coding=utf-8
|
3 |
+
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
|
4 |
+
#
|
5 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
6 |
+
# you may not use this file except in compliance with the License.
|
7 |
+
# You may obtain a copy of the License at
|
8 |
+
#
|
9 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
10 |
+
#
|
11 |
+
# Unless required by applicable law or agreed to in writing, software
|
12 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
13 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
14 |
+
# See the License for the specific language governing permissions and
|
15 |
+
|
16 |
+
""" Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition"""
|
17 |
+
|
18 |
+
import functools
|
19 |
+
import json
|
20 |
+
import logging
|
21 |
+
import os
|
22 |
+
import re
|
23 |
+
import sys
|
24 |
+
import warnings
|
25 |
+
from dataclasses import dataclass, field
|
26 |
+
from typing import Dict, List, Optional, Union
|
27 |
+
|
28 |
+
import datasets
|
29 |
+
import numpy as np
|
30 |
+
import torch
|
31 |
+
from datasets import DatasetDict, load_dataset, load_metric
|
32 |
+
|
33 |
+
import transformers
|
34 |
+
from transformers import (
|
35 |
+
AutoConfig,
|
36 |
+
AutoFeatureExtractor,
|
37 |
+
AutoModelForCTC,
|
38 |
+
AutoProcessor,
|
39 |
+
AutoTokenizer,
|
40 |
+
HfArgumentParser,
|
41 |
+
Trainer,
|
42 |
+
TrainingArguments,
|
43 |
+
Wav2Vec2Processor,
|
44 |
+
set_seed,
|
45 |
+
)
|
46 |
+
from transformers.trainer_utils import get_last_checkpoint, is_main_process
|
47 |
+
from transformers.utils import check_min_version
|
48 |
+
from transformers.utils.versions import require_version
|
49 |
+
|
50 |
+
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
|
51 |
+
check_min_version("4.16.0.dev0")
|
52 |
+
|
53 |
+
require_version("datasets>=1.13.3", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
|
54 |
+
|
55 |
+
logger = logging.getLogger(__name__)
|
56 |
+
|
57 |
+
|
58 |
+
def list_field(default=None, metadata=None):
|
59 |
+
return field(default_factory=lambda: default, metadata=metadata)
|
60 |
+
|
61 |
+
|
62 |
+
@dataclass
|
63 |
+
class ModelArguments:
|
64 |
+
"""
|
65 |
+
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
66 |
+
"""
|
67 |
+
|
68 |
+
model_name_or_path: str = field(
|
69 |
+
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
70 |
+
)
|
71 |
+
tokenizer_name_or_path: Optional[str] = field(
|
72 |
+
default=None,
|
73 |
+
metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
|
74 |
+
)
|
75 |
+
cache_dir: Optional[str] = field(
|
76 |
+
default=None,
|
77 |
+
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
|
78 |
+
)
|
79 |
+
freeze_feature_encoder: bool = field(
|
80 |
+
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
|
81 |
+
)
|
82 |
+
attention_dropout: float = field(
|
83 |
+
default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
|
84 |
+
)
|
85 |
+
activation_dropout: float = field(
|
86 |
+
default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
|
87 |
+
)
|
88 |
+
feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
|
89 |
+
hidden_dropout: float = field(
|
90 |
+
default=0.0,
|
91 |
+
metadata={
|
92 |
+
"help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
|
93 |
+
},
|
94 |
+
)
|
95 |
+
final_dropout: float = field(
|
96 |
+
default=0.0,
|
97 |
+
metadata={"help": "The dropout probability for the final projection layer."},
|
98 |
+
)
|
99 |
+
mask_time_prob: float = field(
|
100 |
+
default=0.05,
|
101 |
+
metadata={
|
102 |
+
"help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
|
103 |
+
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
|
104 |
+
"vectors will be masked along the time axis."
|
105 |
+
},
|
106 |
+
)
|
107 |
+
mask_time_length: int = field(
|
108 |
+
default=10,
|
109 |
+
metadata={"help": "Length of vector span to mask along the time axis."},
|
110 |
+
)
|
111 |
+
mask_feature_prob: float = field(
|
112 |
+
default=0.0,
|
113 |
+
metadata={
|
114 |
+
"help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
|
115 |
+
"span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
|
116 |
+
},
|
117 |
+
)
|
118 |
+
mask_feature_length: int = field(
|
119 |
+
default=10,
|
120 |
+
metadata={"help": "Length of vector span to mask along the feature axis."},
|
121 |
+
)
|
122 |
+
layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
|
123 |
+
ctc_loss_reduction: Optional[str] = field(
|
124 |
+
default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
|
125 |
+
)
|
126 |
+
ctc_zero_infinity: Optional[bool] = field(
|
127 |
+
default=False, metadata={"help": "If True, will try yo aboud the CTC loss goinf to infinity."}
|
128 |
+
)
|
129 |
+
|
130 |
+
|
131 |
+
@dataclass
|
132 |
+
class DataTrainingArguments:
|
133 |
+
"""
|
134 |
+
Arguments pertaining to what data we are going to input our model for training and eval.
|
135 |
+
|
136 |
+
Using `HfArgumentParser` we can turn this class
|
137 |
+
into argparse arguments to be able to specify them on
|
138 |
+
the command line.
|
139 |
+
"""
|
140 |
+
|
141 |
+
# dataset_name: str = field(
|
142 |
+
# metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
143 |
+
# )
|
144 |
+
# dataset_config_name: str = field(
|
145 |
+
# default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
146 |
+
# )
|
147 |
+
train_split_name: str = field(
|
148 |
+
default="train",
|
149 |
+
metadata={
|
150 |
+
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
151 |
+
},
|
152 |
+
)
|
153 |
+
eval_split_name: str = field(
|
154 |
+
default="test",
|
155 |
+
metadata={
|
156 |
+
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
157 |
+
},
|
158 |
+
)
|
159 |
+
audio_column_name: str = field(
|
160 |
+
default="audio",
|
161 |
+
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
|
162 |
+
)
|
163 |
+
text_column_name: str = field(
|
164 |
+
default="text",
|
165 |
+
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
|
166 |
+
)
|
167 |
+
overwrite_cache: bool = field(
|
168 |
+
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
|
169 |
+
)
|
170 |
+
preprocessing_num_workers: Optional[int] = field(
|
171 |
+
default=None,
|
172 |
+
metadata={"help": "The number of processes to use for the preprocessing."},
|
173 |
+
)
|
174 |
+
max_train_samples: Optional[int] = field(
|
175 |
+
default=None,
|
176 |
+
metadata={
|
177 |
+
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
178 |
+
"value if set."
|
179 |
+
},
|
180 |
+
)
|
181 |
+
max_eval_samples: Optional[int] = field(
|
182 |
+
default=None,
|
183 |
+
metadata={
|
184 |
+
"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
|
185 |
+
"value if set."
|
186 |
+
},
|
187 |
+
)
|
188 |
+
chars_to_ignore: Optional[List[str]] = list_field(
|
189 |
+
default=None,
|
190 |
+
metadata={"help": "A list of characters to remove from the transcripts."},
|
191 |
+
)
|
192 |
+
eval_metrics: List[str] = list_field(
|
193 |
+
default=["wer"],
|
194 |
+
metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
|
195 |
+
)
|
196 |
+
max_duration_in_seconds: float = field(
|
197 |
+
default=20.0,
|
198 |
+
metadata={
|
199 |
+
"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
|
200 |
+
},
|
201 |
+
)
|
202 |
+
min_duration_in_seconds: float = field(
|
203 |
+
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
|
204 |
+
)
|
205 |
+
preprocessing_only: bool = field(
|
206 |
+
default=False,
|
207 |
+
metadata={
|
208 |
+
"help": "Whether to only do data preprocessing and skip training. "
|
209 |
+
"This is especially useful when data preprocessing errors out in distributed training due to timeout. "
|
210 |
+
"In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
|
211 |
+
"so that the cached datasets can consequently be loaded in distributed training"
|
212 |
+
},
|
213 |
+
)
|
214 |
+
use_auth_token: bool = field(
|
215 |
+
default=False,
|
216 |
+
metadata={
|
217 |
+
"help": "If :obj:`True`, will use the token generated when running"
|
218 |
+
":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
|
219 |
+
},
|
220 |
+
)
|
221 |
+
unk_token: str = field(
|
222 |
+
default="[UNK]",
|
223 |
+
metadata={"help": "The unk token for the tokenizer"},
|
224 |
+
)
|
225 |
+
pad_token: str = field(
|
226 |
+
default="[PAD]",
|
227 |
+
metadata={"help": "The padding token for the tokenizer"},
|
228 |
+
)
|
229 |
+
word_delimiter_token: str = field(
|
230 |
+
default="|",
|
231 |
+
metadata={"help": "The word delimiter token for the tokenizer"},
|
232 |
+
)
|
233 |
+
phoneme_language: Optional[str] = field(
|
234 |
+
default=None,
|
235 |
+
metadata={
|
236 |
+
"help": "The target language that should be used be"
|
237 |
+
" passed to the tokenizer for tokenization. Note that"
|
238 |
+
" this is only relevant if the model classifies the"
|
239 |
+
" input audio to a sequence of phoneme sequences."
|
240 |
+
},
|
241 |
+
)
|
242 |
+
|
243 |
+
|
244 |
+
@dataclass
|
245 |
+
class DataCollatorCTCWithPadding:
|
246 |
+
"""
|
247 |
+
Data collator that will dynamically pad the inputs received.
|
248 |
+
Args:
|
249 |
+
processor (:class:`~transformers.AutoProcessor`)
|
250 |
+
The processor used for proccessing the data.
|
251 |
+
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
|
252 |
+
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
|
253 |
+
among:
|
254 |
+
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
255 |
+
sequence if provided).
|
256 |
+
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
|
257 |
+
maximum acceptable input length for the model if that argument is not provided.
|
258 |
+
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
|
259 |
+
different lengths).
|
260 |
+
max_length (:obj:`int`, `optional`):
|
261 |
+
Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
|
262 |
+
max_length_labels (:obj:`int`, `optional`):
|
263 |
+
Maximum length of the ``labels`` returned list and optionally padding length (see above).
|
264 |
+
pad_to_multiple_of (:obj:`int`, `optional`):
|
265 |
+
If set will pad the sequence to a multiple of the provided value.
|
266 |
+
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
|
267 |
+
7.5 (Volta).
|
268 |
+
"""
|
269 |
+
|
270 |
+
processor: AutoProcessor
|
271 |
+
padding: Union[bool, str] = "longest"
|
272 |
+
pad_to_multiple_of: Optional[int] = None
|
273 |
+
pad_to_multiple_of_labels: Optional[int] = None
|
274 |
+
|
275 |
+
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
|
276 |
+
# split inputs and labels since they have to be of different lenghts and need
|
277 |
+
# different padding methods
|
278 |
+
input_features = [{"input_values": feature["input_values"]} for feature in features]
|
279 |
+
label_features = [{"input_ids": feature["labels"]} for feature in features]
|
280 |
+
|
281 |
+
batch = self.processor.pad(
|
282 |
+
input_features,
|
283 |
+
padding=self.padding,
|
284 |
+
pad_to_multiple_of=self.pad_to_multiple_of,
|
285 |
+
return_tensors="pt",
|
286 |
+
)
|
287 |
+
|
288 |
+
with self.processor.as_target_processor():
|
289 |
+
labels_batch = self.processor.pad(
|
290 |
+
label_features,
|
291 |
+
padding=self.padding,
|
292 |
+
pad_to_multiple_of=self.pad_to_multiple_of_labels,
|
293 |
+
return_tensors="pt",
|
294 |
+
)
|
295 |
+
|
296 |
+
# replace padding with -100 to ignore loss correctly
|
297 |
+
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
|
298 |
+
|
299 |
+
batch["labels"] = labels
|
300 |
+
|
301 |
+
return batch
|
302 |
+
|
303 |
+
|
304 |
+
def create_vocabulary_from_data(
|
305 |
+
datasets: DatasetDict,
|
306 |
+
word_delimiter_token: Optional[str] = None,
|
307 |
+
unk_token: Optional[str] = None,
|
308 |
+
pad_token: Optional[str] = None,
|
309 |
+
):
|
310 |
+
# Given training and test labels create vocabulary
|
311 |
+
alphabet = set()
|
312 |
+
|
313 |
+
def extract_all_chars(batch):
|
314 |
+
all_text = " ".join(batch["target_text"])
|
315 |
+
alphabet.update(all_text)
|
316 |
+
|
317 |
+
datasets.map(
|
318 |
+
extract_all_chars,
|
319 |
+
batched=True,
|
320 |
+
batch_size=-1,
|
321 |
+
keep_in_memory=True,
|
322 |
+
remove_columns=datasets["train"].column_names,
|
323 |
+
)
|
324 |
+
|
325 |
+
# # take union of all unique characters in each dataset
|
326 |
+
# vocab_set = functools.reduce(
|
327 |
+
# lambda vocab_1, vocab_2: {"vocab": list(set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]))}, vocabs.values()
|
328 |
+
# )["vocab"][0]
|
329 |
+
|
330 |
+
vocab_dict = {v: k for k, v in enumerate(sorted(list(alphabet)))}
|
331 |
+
|
332 |
+
# replace white space with delimiter token
|
333 |
+
if word_delimiter_token is not None:
|
334 |
+
vocab_dict[word_delimiter_token] = vocab_dict[" "]
|
335 |
+
del vocab_dict[" "]
|
336 |
+
|
337 |
+
# add unk and pad token
|
338 |
+
if unk_token is not None:
|
339 |
+
vocab_dict[unk_token] = len(vocab_dict)
|
340 |
+
|
341 |
+
if pad_token is not None:
|
342 |
+
vocab_dict[pad_token] = len(vocab_dict)
|
343 |
+
|
344 |
+
return vocab_dict
|
345 |
+
|
346 |
+
|
347 |
+
def make_dataset(seed=42):
|
348 |
+
# Pre-processing dataset
|
349 |
+
import re
|
350 |
+
|
351 |
+
def replace_strange_characters(text):
|
352 |
+
text = re.sub('[áàâ]', 'a', text)
|
353 |
+
text = re.sub('[ä]', 'æ', text)
|
354 |
+
text = re.sub('[éèëê]', 'e', text)
|
355 |
+
text = re.sub('[íìïî]', 'i', text)
|
356 |
+
text = re.sub('[óòöô]', 'o', text)
|
357 |
+
text = re.sub('[ö]', 'ø', text)
|
358 |
+
text = re.sub('[ç]', 'c', text)
|
359 |
+
text = re.sub('[úùüû]', 'u', text)
|
360 |
+
return text
|
361 |
+
|
362 |
+
def replace_hesitations(text):
|
363 |
+
# text = re.sub("<[^>]*>", " ", text) # <ee>, <qq>, <mm>, <inaudible> for NPSC. <eeeh>, <mmm> for NST-hesitate
|
364 |
+
text = re.sub("<ee(eh)?>", "E", text)
|
365 |
+
text = re.sub("<mmm?>", "M", text)
|
366 |
+
text = re.sub("<qq>", "Q", text)
|
367 |
+
text = re.sub("<inaudible>", "I", text)
|
368 |
+
return text
|
369 |
+
|
370 |
+
def is_too_short(entry):
|
371 |
+
return len(entry["text"]) > len(entry["audio"]["array"]) // 320 or len(entry["text"]) <=1
|
372 |
+
|
373 |
+
def map_nst(entry):
|
374 |
+
text = entry["text"].lower()
|
375 |
+
text = text.replace("(...vær stille under dette opptaket...)", " ")
|
376 |
+
text = replace_hesitations(text)
|
377 |
+
text = replace_strange_characters(text)
|
378 |
+
text = re.sub('\s+', ' ', text)
|
379 |
+
return {"text": text.strip()}
|
380 |
+
|
381 |
+
def filter_nst(entry):
|
382 |
+
if is_too_short(entry):
|
383 |
+
return False # Too short
|
384 |
+
if re.match(entry["type"], "pIW|CA"):
|
385 |
+
return False # Spelling out words
|
386 |
+
if re.search("\d", entry["text"]):
|
387 |
+
return False
|
388 |
+
return True
|
389 |
+
|
390 |
+
def filter_npsc(entry):
|
391 |
+
if is_too_short(entry):
|
392 |
+
return False # Too short
|
393 |
+
if re.search("\d", entry["text"]):
|
394 |
+
return False
|
395 |
+
return True
|
396 |
+
|
397 |
+
def map_npsc(entry):
|
398 |
+
text = entry["text"] if entry["sentence_language_code"].startswith("nn") else entry["normsentence_text"]
|
399 |
+
text = text.lower()
|
400 |
+
text = replace_strange_characters(text)
|
401 |
+
text = replace_hesitations(text)
|
402 |
+
text = re.sub('\s+', ' ', text)
|
403 |
+
return {"text": text.strip()}
|
404 |
+
|
405 |
+
nst = datasets.load_dataset("NbAiLab/NST", "no-close")
|
406 |
+
npsc = datasets.load_dataset("NbAiLab/NPSC", "16K_mp3")
|
407 |
+
nsth = datasets.load_dataset("NbAiLab/NST_hesitate", "no")
|
408 |
+
|
409 |
+
nst = nst.map(map_nst).filter(filter_nst)
|
410 |
+
npsc = npsc.map(map_npsc).filter(filter_npsc)
|
411 |
+
nsth = nsth.map(map_nst).filter(filter_npsc)
|
412 |
+
|
413 |
+
split = len(npsc["train"]) / (len(npsc["train"]) + len(npsc["validation"])) # Use same train/val ratio as NPSC
|
414 |
+
nst_train = nst["train"].train_test_split(train_size=split, seed=seed)
|
415 |
+
nst["train"] = nst_train["train"]
|
416 |
+
nst["validation"] = nst_train["test"]
|
417 |
+
|
418 |
+
nsth_train = nsth["train"].train_test_split(train_size=split, seed=seed)
|
419 |
+
nsth["train"] = nsth_train["train"]
|
420 |
+
nsth["validation"] = nsth_train["test"]
|
421 |
+
|
422 |
+
nst_base = nst.remove_columns([col for col in nst["train"].column_names if col not in ["text", "audio"]])
|
423 |
+
npsc_base = npsc.remove_columns([col for col in npsc["train"].column_names if col not in ["text", "audio"]])
|
424 |
+
nsth_base = nsth.remove_columns([col for col in nsth["train"].column_names if col not in ["text", "audio"]])
|
425 |
+
|
426 |
+
combined = {}
|
427 |
+
for split in "train", "validation", "test":
|
428 |
+
# Weight by number of examples
|
429 |
+
probs = np.array([len(nst_base[split]), len(npsc_base[split]), len(nsth_base[split])])
|
430 |
+
probs = (probs / probs.sum()).tolist()
|
431 |
+
comb = datasets.interleave_datasets([nst_base[split], npsc_base[split], nsth_base[split]],
|
432 |
+
probabilities=probs, seed=seed)
|
433 |
+
combined[split] = comb
|
434 |
+
|
435 |
+
return datasets.DatasetDict(**combined)
|
436 |
+
|
437 |
+
|
438 |
+
def main():
|
439 |
+
# See all possible arguments in src/transformers/training_args.py
|
440 |
+
# or by passing the --help flag to this script.
|
441 |
+
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
442 |
+
|
443 |
+
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
444 |
+
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
445 |
+
# If we pass only one argument to the script and it's the path to a json file,
|
446 |
+
# let's parse it to get our arguments.
|
447 |
+
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
448 |
+
else:
|
449 |
+
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
450 |
+
|
451 |
+
# Detecting last checkpoint.
|
452 |
+
last_checkpoint = None
|
453 |
+
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
454 |
+
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
455 |
+
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
456 |
+
raise ValueError(
|
457 |
+
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
458 |
+
"Use --overwrite_output_dir to overcome."
|
459 |
+
)
|
460 |
+
elif last_checkpoint is not None:
|
461 |
+
logger.info(
|
462 |
+
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
463 |
+
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
464 |
+
)
|
465 |
+
|
466 |
+
# Setup logging
|
467 |
+
logging.basicConfig(
|
468 |
+
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
469 |
+
datefmt="%m/%d/%Y %H:%M:%S",
|
470 |
+
handlers=[logging.StreamHandler(sys.stdout)],
|
471 |
+
)
|
472 |
+
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
|
473 |
+
|
474 |
+
# Log on each process the small summary:
|
475 |
+
logger.warning(
|
476 |
+
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
|
477 |
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
|
478 |
+
)
|
479 |
+
# Set the verbosity to info of the Transformers logger (on main process only):
|
480 |
+
if is_main_process(training_args.local_rank):
|
481 |
+
transformers.utils.logging.set_verbosity_info()
|
482 |
+
logger.info("Training/evaluation parameters %s", training_args)
|
483 |
+
|
484 |
+
# Set seed before initializing model.
|
485 |
+
set_seed(training_args.seed)
|
486 |
+
|
487 |
+
# 1. First, let's load the dataset
|
488 |
+
raw_datasets = make_dataset(seed=training_args.seed)
|
489 |
+
|
490 |
+
if training_args.do_train:
|
491 |
+
if data_args.audio_column_name not in raw_datasets["train"].column_names:
|
492 |
+
raise ValueError(
|
493 |
+
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
|
494 |
+
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
495 |
+
f"{', '.join(raw_datasets['train'].column_names)}."
|
496 |
+
)
|
497 |
+
|
498 |
+
if data_args.text_column_name not in raw_datasets["train"].column_names:
|
499 |
+
raise ValueError(
|
500 |
+
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
|
501 |
+
"Make sure to set `--text_column_name` to the correct text column - one of "
|
502 |
+
f"{', '.join(raw_datasets['train'].column_names)}."
|
503 |
+
)
|
504 |
+
|
505 |
+
if data_args.max_train_samples is not None:
|
506 |
+
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
|
507 |
+
|
508 |
+
if training_args.do_eval:
|
509 |
+
if data_args.max_eval_samples is not None:
|
510 |
+
raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
|
511 |
+
|
512 |
+
# 2. We remove some special characters from the datasets
|
513 |
+
# that make training complicated and do not help in transcribing the speech
|
514 |
+
# E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
|
515 |
+
# that could be easily picked up by the model
|
516 |
+
# chars_to_ignore_regex = (
|
517 |
+
# f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
|
518 |
+
# )
|
519 |
+
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\–\_\\\+\#\/]'
|
520 |
+
|
521 |
+
text_column_name = data_args.text_column_name
|
522 |
+
|
523 |
+
def remove_special_characters(batch):
|
524 |
+
if chars_to_ignore_regex is not None:
|
525 |
+
batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
|
526 |
+
else:
|
527 |
+
batch["target_text"] = batch[text_column_name].lower() + " "
|
528 |
+
return batch
|
529 |
+
|
530 |
+
with training_args.main_process_first(desc="dataset map special characters removal"):
|
531 |
+
raw_datasets = raw_datasets.map(
|
532 |
+
remove_special_characters,
|
533 |
+
remove_columns=[text_column_name],
|
534 |
+
desc="remove special characters from datasets",
|
535 |
+
)
|
536 |
+
|
537 |
+
# save special tokens for tokenizer
|
538 |
+
word_delimiter_token = data_args.word_delimiter_token
|
539 |
+
unk_token = data_args.unk_token
|
540 |
+
pad_token = data_args.pad_token
|
541 |
+
|
542 |
+
# 3. Next, let's load the config as we might need it to create
|
543 |
+
# the tokenizer
|
544 |
+
# load config
|
545 |
+
config = AutoConfig.from_pretrained(
|
546 |
+
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
|
547 |
+
)
|
548 |
+
|
549 |
+
# 4. Next, if no tokenizer file is defined,
|
550 |
+
# we create the vocabulary of the model by extracting all unique characters from
|
551 |
+
# the training and evaluation datasets
|
552 |
+
# We need to make sure that only first rank saves vocabulary
|
553 |
+
# make sure all processes wait until vocab is created
|
554 |
+
tokenizer_name_or_path = model_args.tokenizer_name_or_path
|
555 |
+
tokenizer_kwargs = {}
|
556 |
+
if tokenizer_name_or_path is None:
|
557 |
+
# save vocab in training output dir
|
558 |
+
tokenizer_name_or_path = training_args.output_dir
|
559 |
+
|
560 |
+
vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
|
561 |
+
|
562 |
+
with training_args.main_process_first():
|
563 |
+
if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
|
564 |
+
os.remove(vocab_file)
|
565 |
+
|
566 |
+
with training_args.main_process_first(desc="dataset map vocabulary creation"):
|
567 |
+
if not os.path.isfile(vocab_file):
|
568 |
+
os.makedirs(tokenizer_name_or_path, exist_ok=True)
|
569 |
+
vocab_dict = create_vocabulary_from_data(
|
570 |
+
raw_datasets,
|
571 |
+
word_delimiter_token=word_delimiter_token,
|
572 |
+
unk_token=unk_token,
|
573 |
+
pad_token=pad_token,
|
574 |
+
)
|
575 |
+
|
576 |
+
# save vocab dict to be loaded into tokenizer
|
577 |
+
with open(vocab_file, "w") as file:
|
578 |
+
json.dump(vocab_dict, file)
|
579 |
+
|
580 |
+
# if tokenizer has just been created
|
581 |
+
# it is defined by `tokenizer_class` if present in config else by `model_type`
|
582 |
+
tokenizer_kwargs = {
|
583 |
+
"config": config if config.tokenizer_class is not None else None,
|
584 |
+
"tokenizer_type": config.model_type if config.tokenizer_class is None else None,
|
585 |
+
"unk_token": unk_token,
|
586 |
+
"pad_token": pad_token,
|
587 |
+
"word_delimiter_token": word_delimiter_token,
|
588 |
+
}
|
589 |
+
|
590 |
+
# 5. Now we can instantiate the feature extractor, tokenizer and model
|
591 |
+
# Note for distributed training, the .from_pretrained methods guarantee that only
|
592 |
+
# one local process can concurrently download model & vocab.
|
593 |
+
|
594 |
+
# load feature_extractor and tokenizer
|
595 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
596 |
+
tokenizer_name_or_path,
|
597 |
+
use_auth_token=data_args.use_auth_token,
|
598 |
+
**tokenizer_kwargs,
|
599 |
+
)
|
600 |
+
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
601 |
+
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
|
602 |
+
)
|
603 |
+
|
604 |
+
# adapt config
|
605 |
+
config.update(
|
606 |
+
{
|
607 |
+
"feat_proj_dropout": model_args.feat_proj_dropout,
|
608 |
+
"attention_dropout": model_args.attention_dropout,
|
609 |
+
"hidden_dropout": model_args.hidden_dropout,
|
610 |
+
"final_dropout": model_args.final_dropout,
|
611 |
+
"mask_time_prob": model_args.mask_time_prob,
|
612 |
+
"mask_time_length": model_args.mask_time_length,
|
613 |
+
"mask_feature_prob": model_args.mask_feature_prob,
|
614 |
+
"mask_feature_length": model_args.mask_feature_length,
|
615 |
+
"gradient_checkpointing": training_args.gradient_checkpointing,
|
616 |
+
"layerdrop": model_args.layerdrop,
|
617 |
+
"ctc_loss_reduction": model_args.ctc_loss_reduction,
|
618 |
+
"ctc_zero_infinity": model_args.ctc_zero_infinity,
|
619 |
+
"pad_token_id": tokenizer.pad_token_id,
|
620 |
+
"vocab_size": len(tokenizer),
|
621 |
+
"activation_dropout": model_args.activation_dropout,
|
622 |
+
}
|
623 |
+
)
|
624 |
+
|
625 |
+
# create model
|
626 |
+
model = AutoModelForCTC.from_pretrained(
|
627 |
+
model_args.model_name_or_path,
|
628 |
+
cache_dir=model_args.cache_dir,
|
629 |
+
config=config,
|
630 |
+
use_auth_token=data_args.use_auth_token,
|
631 |
+
)
|
632 |
+
|
633 |
+
# freeze encoder
|
634 |
+
if model_args.freeze_feature_encoder:
|
635 |
+
model.freeze_feature_encoder()
|
636 |
+
|
637 |
+
# 6. Now we preprocess the datasets including loading the audio, resampling and normalization
|
638 |
+
# Thankfully, `datasets` takes care of automatically loading and resampling the audio,
|
639 |
+
# so that we just need to set the correct target sampling rate and normalize the input
|
640 |
+
# via the `feature_extractor`
|
641 |
+
|
642 |
+
# make sure that dataset decodes audio with correct sampling rate
|
643 |
+
dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
|
644 |
+
if dataset_sampling_rate != feature_extractor.sampling_rate:
|
645 |
+
raw_datasets = raw_datasets.cast_column(
|
646 |
+
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
|
647 |
+
)
|
648 |
+
|
649 |
+
# derive max & min input length for sample rate & max duration
|
650 |
+
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
|
651 |
+
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
|
652 |
+
audio_column_name = data_args.audio_column_name
|
653 |
+
num_workers = data_args.preprocessing_num_workers
|
654 |
+
|
655 |
+
# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
|
656 |
+
phoneme_language = data_args.phoneme_language
|
657 |
+
|
658 |
+
# Preprocessing the datasets.
|
659 |
+
# We need to read the audio files as arrays and tokenize the targets.
|
660 |
+
def prepare_dataset(batch):
|
661 |
+
# load audio
|
662 |
+
sample = batch[audio_column_name]
|
663 |
+
|
664 |
+
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
|
665 |
+
batch["input_values"] = inputs.input_values[0]
|
666 |
+
batch["input_length"] = len(batch["input_values"])
|
667 |
+
|
668 |
+
# encode targets
|
669 |
+
additional_kwargs = {}
|
670 |
+
if phoneme_language is not None:
|
671 |
+
additional_kwargs["phonemizer_lang"] = phoneme_language
|
672 |
+
|
673 |
+
batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
|
674 |
+
return batch
|
675 |
+
|
676 |
+
with training_args.main_process_first(desc="dataset map preprocessing"):
|
677 |
+
vectorized_datasets = raw_datasets.map(
|
678 |
+
prepare_dataset,
|
679 |
+
remove_columns=next(iter(raw_datasets.values())).column_names,
|
680 |
+
num_proc=num_workers,
|
681 |
+
desc="preprocess datasets",
|
682 |
+
)
|
683 |
+
|
684 |
+
def is_audio_in_length_range(length):
|
685 |
+
return length > min_input_length and length < max_input_length
|
686 |
+
|
687 |
+
# filter data that is shorter than min_input_length
|
688 |
+
vectorized_datasets = vectorized_datasets.filter(
|
689 |
+
is_audio_in_length_range,
|
690 |
+
num_proc=num_workers,
|
691 |
+
input_columns=["input_length"],
|
692 |
+
)
|
693 |
+
|
694 |
+
# 7. Next, we can prepare the training.
|
695 |
+
# Let's use word error rate (WER) as our evaluation metric,
|
696 |
+
# instantiate a data collator and the trainer
|
697 |
+
|
698 |
+
# Define evaluation metrics during training, *i.e.* word error rate, character error rate
|
699 |
+
eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
|
700 |
+
|
701 |
+
# for large datasets it is advised to run the preprocessing on a
|
702 |
+
# single machine first with ``args.preprocessing_only`` since there will mostly likely
|
703 |
+
# be a timeout when running the script in distributed mode.
|
704 |
+
# In a second step ``args.preprocessing_only`` can then be set to `False` to load the
|
705 |
+
# cached dataset
|
706 |
+
if data_args.preprocessing_only:
|
707 |
+
logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
|
708 |
+
return
|
709 |
+
|
710 |
+
def compute_metrics(pred):
|
711 |
+
pred_logits = pred.predictions
|
712 |
+
pred_ids = np.argmax(pred_logits, axis=-1)
|
713 |
+
|
714 |
+
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
|
715 |
+
|
716 |
+
pred_str = tokenizer.batch_decode(pred_ids)
|
717 |
+
# we do not want to group tokens when computing the metrics
|
718 |
+
label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
|
719 |
+
|
720 |
+
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
|
721 |
+
|
722 |
+
return metrics
|
723 |
+
|
724 |
+
# Now save everything to be able to create a single processor later
|
725 |
+
if is_main_process(training_args.local_rank):
|
726 |
+
# save feature extractor, tokenizer and config
|
727 |
+
feature_extractor.save_pretrained(training_args.output_dir)
|
728 |
+
tokenizer.save_pretrained(training_args.output_dir)
|
729 |
+
config.save_pretrained(training_args.output_dir)
|
730 |
+
|
731 |
+
try:
|
732 |
+
processor = AutoProcessor.from_pretrained(training_args.output_dir)
|
733 |
+
except (OSError, KeyError):
|
734 |
+
warnings.warn(
|
735 |
+
"Loading a processor from a feature extractor config that does not"
|
736 |
+
" include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
|
737 |
+
" attribute to your `preprocessor_config.json` file to suppress this warning: "
|
738 |
+
" `'processor_class': 'Wav2Vec2Processor'`",
|
739 |
+
FutureWarning,
|
740 |
+
)
|
741 |
+
processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
|
742 |
+
|
743 |
+
# Instantiate custom data collator
|
744 |
+
data_collator = DataCollatorCTCWithPadding(processor=processor)
|
745 |
+
|
746 |
+
# Initialize Trainer
|
747 |
+
trainer = Trainer(
|
748 |
+
model=model,
|
749 |
+
data_collator=data_collator,
|
750 |
+
args=training_args,
|
751 |
+
compute_metrics=compute_metrics,
|
752 |
+
train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
|
753 |
+
eval_dataset=vectorized_datasets["validation"] if training_args.do_eval else None,
|
754 |
+
tokenizer=feature_extractor,
|
755 |
+
)
|
756 |
+
|
757 |
+
# 8. Finally, we can start training
|
758 |
+
|
759 |
+
# Training
|
760 |
+
if training_args.do_train:
|
761 |
+
|
762 |
+
# use last checkpoint if exist
|
763 |
+
if last_checkpoint is not None:
|
764 |
+
checkpoint = last_checkpoint
|
765 |
+
elif os.path.isdir(model_args.model_name_or_path):
|
766 |
+
checkpoint = model_args.model_name_or_path
|
767 |
+
else:
|
768 |
+
checkpoint = None
|
769 |
+
|
770 |
+
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
771 |
+
trainer.save_model()
|
772 |
+
|
773 |
+
metrics = train_result.metrics
|
774 |
+
max_train_samples = (
|
775 |
+
data_args.max_train_samples
|
776 |
+
if data_args.max_train_samples is not None
|
777 |
+
else len(vectorized_datasets["train"])
|
778 |
+
)
|
779 |
+
metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
|
780 |
+
|
781 |
+
trainer.log_metrics("train", metrics)
|
782 |
+
trainer.save_metrics("train", metrics)
|
783 |
+
trainer.save_state()
|
784 |
+
|
785 |
+
# Evaluation
|
786 |
+
results = {}
|
787 |
+
if training_args.do_eval:
|
788 |
+
logger.info("*** Evaluate ***")
|
789 |
+
metrics = trainer.evaluate()
|
790 |
+
max_eval_samples = (
|
791 |
+
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
|
792 |
+
)
|
793 |
+
metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
|
794 |
+
|
795 |
+
trainer.log_metrics("eval", metrics)
|
796 |
+
trainer.save_metrics("eval", metrics)
|
797 |
+
|
798 |
+
# Write model card and (optionally) push to hub
|
799 |
+
config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
|
800 |
+
kwargs = {
|
801 |
+
"finetuned_from": model_args.model_name_or_path,
|
802 |
+
"tasks": "speech-recognition",
|
803 |
+
"tags": ["automatic-speech-recognition", data_args.dataset_name],
|
804 |
+
"dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
|
805 |
+
"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
|
806 |
+
}
|
807 |
+
if "common_voice" in data_args.dataset_name:
|
808 |
+
kwargs["language"] = config_name
|
809 |
+
|
810 |
+
if training_args.push_to_hub:
|
811 |
+
trainer.push_to_hub(**kwargs)
|
812 |
+
else:
|
813 |
+
trainer.create_model_card(**kwargs)
|
814 |
+
|
815 |
+
return results
|
816 |
+
|
817 |
+
|
818 |
+
if __name__ == "__main__":
|
819 |
+
main()
|
special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]}
|
tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "replace_word_delimiter_char": " ", "special_tokens_map_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer"}
|
training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:23b4bcfd09d8da01d82d5123ed1f20b17a5cbd32a53ff26e65918f2c953ba6b7
|
3 |
+
size 3055
|
vocab.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6, "g": 7, "h": 8, "i": 9, "j": 10, "k": 11, "l": 12, "m": 13, "n": 14, "o": 15, "p": 16, "q": 17, "r": 18, "s": 19, "t": 20, "u": 21, "v": 22, "w": 23, "x": 24, "y": 25, "z": 26, "å": 27, "æ": 28, "ø": 29, "|": 0, "[UNK]": 30, "[PAD]": 31}
|
wandb/debug-internal.log
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
run-20220523_115145-3dybzmyz/logs/debug-internal.log
|
wandb/debug.log
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
run-20220523_115145-3dybzmyz/logs/debug.log
|
wandb/latest-run
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
run-20220523_115145-3dybzmyz
|
wandb/run-20220523_091609-1iboydmy/files/config.yaml
ADDED
The diff for this file is too large to render.
See raw diff
|
|
wandb/run-20220523_091609-1iboydmy/files/output.log
ADDED
@@ -0,0 +1,1379 @@
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0%|▏ | 500/503920 [18:58<122:35:54, 1.14it/s]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.
|
426 |
+
***** Running Evaluation *****
|
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Num examples = 41040
|
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Batch size = 12
|
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0%| | 0/3420 [00:00<?, ?it/s]
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File "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py", line 819, in <module> | 2355/3420 [30:44<12:33, 1.41it/s]
|
1340 |
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main()
|
1341 |
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File "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py", line 770, in main
|
1342 |
+
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
1343 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 1497, in train
|
1344 |
+
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
|
1345 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 1624, in _maybe_log_save_evaluate
|
1346 |
+
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
|
1347 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 2284, in evaluate
|
1348 |
+
output = eval_loop(
|
1349 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 2458, in evaluation_loop
|
1350 |
+
loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
|
1351 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 2671, in prediction_step
|
1352 |
+
loss, outputs = self.compute_loss(model, inputs, return_outputs=True)
|
1353 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 2043, in compute_loss
|
1354 |
+
outputs = model(**inputs)
|
1355 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
|
1356 |
+
return forward_call(*input, **kwargs)
|
1357 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1716, in forward
|
1358 |
+
outputs = self.wav2vec2(
|
1359 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
|
1360 |
+
return forward_call(*input, **kwargs)
|
1361 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1361, in forward
|
1362 |
+
encoder_outputs = self.encoder(
|
1363 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
|
1364 |
+
return forward_call(*input, **kwargs)
|
1365 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 930, in forward
|
1366 |
+
layer_outputs = layer(
|
1367 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
|
1368 |
+
return forward_call(*input, **kwargs)
|
1369 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 766, in forward
|
1370 |
+
hidden_states, attn_weights, _ = self.attention(
|
1371 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
|
1372 |
+
return forward_call(*input, **kwargs)
|
1373 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 686, in forward
|
1374 |
+
attn_output = self.out_proj(attn_output)
|
1375 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
|
1376 |
+
return forward_call(*input, **kwargs)
|
1377 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/nn/modules/linear.py", line 103, in forward
|
1378 |
+
return F.linear(input, self.weight, self.bias)
|
1379 |
+
KeyboardInterrupt
|
wandb/run-20220523_091609-1iboydmy/files/requirements.txt
ADDED
@@ -0,0 +1,77 @@
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|
|
1 |
+
aiohttp==3.8.1
|
2 |
+
aiosignal==1.2.0
|
3 |
+
appdirs==1.4.4
|
4 |
+
async-timeout==4.0.2
|
5 |
+
attrs==21.4.0
|
6 |
+
audioread==2.1.9
|
7 |
+
certifi==2021.10.8
|
8 |
+
cffi==1.15.0
|
9 |
+
charset-normalizer==2.0.12
|
10 |
+
click==8.1.2
|
11 |
+
datasets==2.1.0
|
12 |
+
decorator==5.1.1
|
13 |
+
dill==0.3.4
|
14 |
+
docker-pycreds==0.4.0
|
15 |
+
filelock==3.6.0
|
16 |
+
frozenlist==1.3.0
|
17 |
+
fsspec==2022.3.0
|
18 |
+
gitdb==4.0.9
|
19 |
+
gitpython==3.1.27
|
20 |
+
huggingface-hub==0.5.1
|
21 |
+
hypothesis==6.46.5
|
22 |
+
idna==3.3
|
23 |
+
jiwer==2.3.0
|
24 |
+
joblib==1.1.0
|
25 |
+
kenlm==0.0.0
|
26 |
+
librosa==0.9.1
|
27 |
+
llvmlite==0.38.0
|
28 |
+
multidict==6.0.2
|
29 |
+
multiprocess==0.70.12.2
|
30 |
+
numba==0.55.1
|
31 |
+
numpy==1.21.6
|
32 |
+
packaging==21.3
|
33 |
+
pandas==1.4.2
|
34 |
+
pathtools==0.1.2
|
35 |
+
pillow==9.1.0
|
36 |
+
pip==20.3.4
|
37 |
+
pkg-resources==0.0.0
|
38 |
+
pooch==1.6.0
|
39 |
+
promise==2.3
|
40 |
+
protobuf==3.20.1
|
41 |
+
psutil==5.9.0
|
42 |
+
pyarrow==7.0.0
|
43 |
+
pycparser==2.21
|
44 |
+
pyctcdecode==0.3.0
|
45 |
+
pygtrie==2.4.2
|
46 |
+
pyparsing==3.0.8
|
47 |
+
python-dateutil==2.8.2
|
48 |
+
python-levenshtein==0.12.2
|
49 |
+
pytz==2022.1
|
50 |
+
pyyaml==6.0
|
51 |
+
regex==2022.4.24
|
52 |
+
requests==2.27.1
|
53 |
+
resampy==0.2.2
|
54 |
+
responses==0.18.0
|
55 |
+
sacremoses==0.0.49
|
56 |
+
scikit-learn==1.0.2
|
57 |
+
scipy==1.8.0
|
58 |
+
sentry-sdk==1.5.10
|
59 |
+
setproctitle==1.2.3
|
60 |
+
setuptools==44.1.1
|
61 |
+
shortuuid==1.0.8
|
62 |
+
six==1.16.0
|
63 |
+
smmap==5.0.0
|
64 |
+
sortedcontainers==2.4.0
|
65 |
+
soundfile==0.10.3.post1
|
66 |
+
threadpoolctl==3.1.0
|
67 |
+
tokenizers==0.12.1
|
68 |
+
torch==1.11.0+cu113
|
69 |
+
torchaudio==0.11.0+cu113
|
70 |
+
torchvision==0.12.0+cu113
|
71 |
+
tqdm==4.64.0
|
72 |
+
transformers==4.18.0
|
73 |
+
typing-extensions==4.2.0
|
74 |
+
urllib3==1.26.9
|
75 |
+
wandb==0.12.15
|
76 |
+
xxhash==3.0.0
|
77 |
+
yarl==1.7.2
|
wandb/run-20220523_091609-1iboydmy/files/wandb-metadata.json
ADDED
@@ -0,0 +1,62 @@
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|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"os": "Linux-5.13.0-40-generic-x86_64-with-glibc2.34",
|
3 |
+
"python": "3.9.7",
|
4 |
+
"heartbeatAt": "2022-05-23T07:16:10.988629",
|
5 |
+
"startedAt": "2022-05-23T07:16:09.920284",
|
6 |
+
"docker": null,
|
7 |
+
"cpu_count": 96,
|
8 |
+
"cuda": null,
|
9 |
+
"args": [
|
10 |
+
"--model_name_or_path=facebook/wav2vec2-xls-r-1b",
|
11 |
+
"--hub_model_id=NbAiLab/wav2vec2-1b-npsc-nst-bokmaal",
|
12 |
+
"--output_dir=./",
|
13 |
+
"--overwrite_output_dir",
|
14 |
+
"--num_train_epochs=40",
|
15 |
+
"--per_device_train_batch_size=12",
|
16 |
+
"--per_device_eval_batch_size=12",
|
17 |
+
"--gradient_accumulation_steps=2",
|
18 |
+
"--learning_rate=2e-5",
|
19 |
+
"--warmup_steps=2000",
|
20 |
+
"--length_column_name=input_length",
|
21 |
+
"--evaluation_strategy=steps",
|
22 |
+
"--text_column_name=text",
|
23 |
+
"--save_steps=500",
|
24 |
+
"--eval_steps=500",
|
25 |
+
"--logging_steps=100",
|
26 |
+
"--layerdrop=0.041",
|
27 |
+
"--attention_dropout=0.094",
|
28 |
+
"--activation_dropout=0.055",
|
29 |
+
"--hidden_dropout=0.047",
|
30 |
+
"--save_total_limit=3",
|
31 |
+
"--freeze_feature_encoder",
|
32 |
+
"--feat_proj_dropout=0.04",
|
33 |
+
"--mask_time_prob=0.082",
|
34 |
+
"--mask_time_length=10",
|
35 |
+
"--mask_feature_prob=0.25",
|
36 |
+
"--mask_feature_length=64",
|
37 |
+
"--gradient_checkpointing",
|
38 |
+
"--min_duration_in_seconds=0.5",
|
39 |
+
"--max_duration_in_seconds=30.0",
|
40 |
+
"--use_auth_token",
|
41 |
+
"--seed=42",
|
42 |
+
"--fp16",
|
43 |
+
"--group_by_length",
|
44 |
+
"--do_train",
|
45 |
+
"--do_eval",
|
46 |
+
"--push_to_hub",
|
47 |
+
"--preprocessing_num_workers=32",
|
48 |
+
"--ctc_zero_infinity"
|
49 |
+
],
|
50 |
+
"state": "running",
|
51 |
+
"program": "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py",
|
52 |
+
"codePath": "run_speech_recognition_ctc.py",
|
53 |
+
"git": {
|
54 |
+
"remote": "https://huggingface.co/NbAiLab/wav2vec2-1b-npsc-nst-bokmaal",
|
55 |
+
"commit": "05ba5dd22c9cec7365fba75fae8630eec1302aff"
|
56 |
+
},
|
57 |
+
"email": "[email protected]",
|
58 |
+
"root": "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal",
|
59 |
+
"host": "dante",
|
60 |
+
"username": "rolvb",
|
61 |
+
"executable": "/mnt/lv_ai_1_dante/ml/rolvb/venv/bin/python"
|
62 |
+
}
|
wandb/run-20220523_091609-1iboydmy/files/wandb-summary.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
wandb/run-20220523_091609-1iboydmy/logs/debug-internal.log
ADDED
The diff for this file is too large to render.
See raw diff
|
|
wandb/run-20220523_091609-1iboydmy/logs/debug.log
ADDED
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|
1 |
+
2022-05-23 09:16:09,931 INFO MainThread:1046672 [wandb_setup.py:_flush():75] Loading settings from /home/rolvb/.config/wandb/settings
|
2 |
+
2022-05-23 09:16:09,931 INFO MainThread:1046672 [wandb_setup.py:_flush():75] Loading settings from /mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/wandb/settings
|
3 |
+
2022-05-23 09:16:09,931 INFO MainThread:1046672 [wandb_setup.py:_flush():75] Loading settings from environment variables: {'project': 'wav2vec2', 'entity': 'NbAiLab'}
|
4 |
+
2022-05-23 09:16:09,931 INFO MainThread:1046672 [wandb_setup.py:_flush():75] Inferring run settings from compute environment: {'program_relpath': 'run_speech_recognition_ctc.py', 'program': '/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py'}
|
5 |
+
2022-05-23 09:16:09,931 INFO MainThread:1046672 [wandb_init.py:_log_setup():437] Logging user logs to /mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/wandb/run-20220523_091609-1iboydmy/logs/debug.log
|
6 |
+
2022-05-23 09:16:09,931 INFO MainThread:1046672 [wandb_init.py:_log_setup():438] Logging internal logs to /mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/wandb/run-20220523_091609-1iboydmy/logs/debug-internal.log
|
7 |
+
2022-05-23 09:16:09,932 INFO MainThread:1046672 [wandb_init.py:init():471] calling init triggers
|
8 |
+
2022-05-23 09:16:09,932 INFO MainThread:1046672 [wandb_init.py:init():474] wandb.init called with sweep_config: {}
|
9 |
+
config: {}
|
10 |
+
2022-05-23 09:16:09,932 INFO MainThread:1046672 [wandb_init.py:init():524] starting backend
|
11 |
+
2022-05-23 09:16:09,932 INFO MainThread:1046672 [backend.py:_multiprocessing_setup():97] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
|
12 |
+
2022-05-23 09:16:10,011 INFO MainThread:1046672 [backend.py:ensure_launched():217] starting backend process...
|
13 |
+
2022-05-23 09:16:10,086 INFO MainThread:1046672 [backend.py:ensure_launched():222] started backend process with pid: 1047416
|
14 |
+
2022-05-23 09:16:10,088 INFO MainThread:1046672 [wandb_init.py:init():533] backend started and connected
|
15 |
+
2022-05-23 09:16:10,099 INFO MainThread:1046672 [wandb_init.py:init():597] updated telemetry
|
16 |
+
2022-05-23 09:16:10,290 INFO MainThread:1046672 [wandb_init.py:init():628] communicating run to backend with 30 second timeout
|
17 |
+
2022-05-23 09:16:10,843 INFO MainThread:1046672 [wandb_run.py:_on_init():1923] communicating current version
|
18 |
+
2022-05-23 09:16:10,973 INFO MainThread:1046672 [wandb_run.py:_on_init():1927] got version response upgrade_message: "wandb version 0.12.16 is available! To upgrade, please run:\n $ pip install wandb --upgrade"
|
19 |
+
|
20 |
+
2022-05-23 09:16:10,973 INFO MainThread:1046672 [wandb_init.py:init():659] starting run threads in backend
|
21 |
+
2022-05-23 09:16:11,023 INFO MainThread:1046672 [wandb_run.py:_console_start():1897] atexit reg
|
22 |
+
2022-05-23 09:16:11,024 INFO MainThread:1046672 [wandb_run.py:_redirect():1770] redirect: SettingsConsole.REDIRECT
|
23 |
+
2022-05-23 09:16:11,024 INFO MainThread:1046672 [wandb_run.py:_redirect():1775] Redirecting console.
|
24 |
+
2022-05-23 09:16:11,027 INFO MainThread:1046672 [wandb_run.py:_redirect():1831] Redirects installed.
|
25 |
+
2022-05-23 09:16:11,027 INFO MainThread:1046672 [wandb_init.py:init():684] run started, returning control to user process
|
26 |
+
2022-05-23 09:16:11,051 INFO MainThread:1046672 [wandb_run.py:_config_callback():1131] config_cb None None {'return_dict': True, 'output_hidden_states': False, 'output_attentions': False, 'torchscript': False, 'torch_dtype': 'float32', 'use_bfloat16': False, 'pruned_heads': {}, 'tie_word_embeddings': True, 'is_encoder_decoder': False, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 'tie_encoder_decoder': False, 'max_length': 20, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'chunk_size_feed_forward': 0, 'output_scores': False, 'return_dict_in_generate': False, 'forced_bos_token_id': None, 'forced_eos_token_id': None, 'remove_invalid_values': False, 'exponential_decay_length_penalty': None, 'architectures': ['Wav2Vec2ForPreTraining'], 'finetuning_task': None, 'id2label': {0: 'LABEL_0', 1: 'LABEL_1'}, 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, 'tokenizer_class': None, 'prefix': None, 'bos_token_id': 1, 'pad_token_id': 31, 'eos_token_id': 2, 'sep_token_id': None, 'decoder_start_token_id': None, 'task_specific_params': None, 'problem_type': None, '_name_or_path': 'facebook/wav2vec2-xls-r-1b', 'transformers_version': '4.18.0', 'feat_extract_dropout': 0.0, 'model_type': 'wav2vec2', 'num_feat_extract_layers': 7, 'hidden_size': 1280, 'feat_extract_norm': 'layer', 'feat_extract_activation': 'gelu', 'conv_dim': [512, 512, 512, 512, 512, 512, 512], 'conv_stride': [5, 2, 2, 2, 2, 2, 2], 'conv_kernel': [10, 3, 3, 3, 3, 2, 2], 'conv_bias': True, 'num_conv_pos_embeddings': 128, 'num_conv_pos_embedding_groups': 16, 'num_hidden_layers': 48, 'intermediate_size': 5120, 'hidden_act': 'gelu', 'num_attention_heads': 16, 'hidden_dropout': 0.047, 'attention_dropout': 0.094, 'activation_dropout': 0.055, 'feat_proj_dropout': 0.04, 'final_dropout': 0.0, 'layerdrop': 0.041, 'layer_norm_eps': 1e-05, 'initializer_range': 0.02, 'vocab_size': 34, 'do_stable_layer_norm': True, 'use_weighted_layer_sum': False, 'apply_spec_augment': True, 'mask_time_prob': 0.082, 'mask_time_length': 10, 'mask_time_min_masks': 2, 'mask_feature_prob': 0.25, 'mask_feature_length': 64, 'mask_feature_min_masks': 0, 'num_codevectors_per_group': 320, 'num_codevector_groups': 2, 'contrastive_logits_temperature': 0.1, 'feat_quantizer_dropout': 0.0, 'num_negatives': 100, 'codevector_dim': 1024, 'proj_codevector_dim': 1024, 'diversity_loss_weight': 0.1, 'ctc_loss_reduction': 'mean', 'ctc_zero_infinity': True, 'add_adapter': False, 'adapter_kernel_size': 3, 'adapter_stride': 2, 'num_adapter_layers': 3, 'output_hidden_size': 1280, 'classifier_proj_size': 256, 'tdnn_dim': [512, 512, 512, 512, 1500], 'tdnn_kernel': [5, 3, 3, 1, 1], 'tdnn_dilation': [1, 2, 3, 1, 1], 'xvector_output_dim': 512, 'output_dir': './', 'overwrite_output_dir': True, 'do_train': True, 'do_eval': True, 'do_predict': False, 'evaluation_strategy': 'steps', 'prediction_loss_only': False, 'per_device_train_batch_size': 12, 'per_device_eval_batch_size': 12, 'per_gpu_train_batch_size': 'None', 'per_gpu_eval_batch_size': 'None', 'gradient_accumulation_steps': 2, 'eval_accumulation_steps': 'None', 'eval_delay': 0, 'learning_rate': 2e-05, 'weight_decay': 0.0, 'adam_beta1': 0.9, 'adam_beta2': 0.999, 'adam_epsilon': 1e-08, 'max_grad_norm': 1.0, 'num_train_epochs': 40.0, 'max_steps': -1, 'lr_scheduler_type': 'linear', 'warmup_ratio': 0.0, 'warmup_steps': 2000, 'log_level': -1, 'log_level_replica': -1, 'log_on_each_node': True, 'logging_dir': './runs/May23_08-59-49_dante', 'logging_strategy': 'steps', 'logging_first_step': False, 'logging_steps': 100, 'logging_nan_inf_filter': True, 'save_strategy': 'steps', 'save_steps': 500, 'save_total_limit': 3, 'save_on_each_node': False, 'no_cuda': False, 'seed': 42, 'data_seed': 'None', 'bf16': False, 'fp16': True, 'fp16_opt_level': 'O1', 'half_precision_backend': 'amp', 'bf16_full_eval': False, 'fp16_full_eval': False, 'tf32': 'None', 'local_rank': -1, 'xpu_backend': 'None', 'tpu_num_cores': 'None', 'tpu_metrics_debug': False, 'debug': '[]', 'dataloader_drop_last': False, 'eval_steps': 500, 'dataloader_num_workers': 0, 'past_index': -1, 'run_name': './', 'disable_tqdm': False, 'remove_unused_columns': True, 'label_names': 'None', 'load_best_model_at_end': False, 'metric_for_best_model': 'None', 'greater_is_better': 'None', 'ignore_data_skip': False, 'sharded_ddp': '[]', 'deepspeed': 'None', 'label_smoothing_factor': 0.0, 'optim': 'adamw_hf', 'adafactor': False, 'group_by_length': True, 'length_column_name': 'input_length', 'report_to': "['wandb']", 'ddp_find_unused_parameters': 'None', 'ddp_bucket_cap_mb': 'None', 'dataloader_pin_memory': True, 'skip_memory_metrics': True, 'use_legacy_prediction_loop': False, 'push_to_hub': True, 'resume_from_checkpoint': 'None', 'hub_model_id': 'NbAiLab/wav2vec2-1b-npsc-nst-bokmaal', 'hub_strategy': 'every_save', 'hub_token': '<HUB_TOKEN>', 'gradient_checkpointing': True, 'fp16_backend': 'auto', 'push_to_hub_model_id': 'None', 'push_to_hub_organization': 'None', 'push_to_hub_token': '<PUSH_TO_HUB_TOKEN>', '_n_gpu': 1, 'mp_parameters': '', 'train_batch_size': 12, 'eval_batch_size': 12}
|
27 |
+
2022-05-23 09:16:11,054 INFO MainThread:1046672 [wandb_watch.py:watch():47] Watching
|
28 |
+
2022-05-23 10:05:55,537 INFO MainThread:1046672 [wandb_run.py:_atexit_cleanup():1866] got exitcode: 255
|
29 |
+
2022-05-23 10:05:55,539 INFO MainThread:1046672 [wandb_run.py:_restore():1838] restore
|
30 |
+
2022-05-23 10:05:58,337 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
31 |
+
wandb_count: 1
|
32 |
+
}
|
33 |
+
pusher_stats {
|
34 |
+
uploaded_bytes: 2176
|
35 |
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total_bytes: 2176
|
36 |
+
}
|
37 |
+
|
38 |
+
2022-05-23 10:05:58,442 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
39 |
+
wandb_count: 1
|
40 |
+
}
|
41 |
+
pusher_stats {
|
42 |
+
uploaded_bytes: 2176
|
43 |
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total_bytes: 2176
|
44 |
+
}
|
45 |
+
|
46 |
+
2022-05-23 10:05:58,584 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
47 |
+
wandb_count: 1
|
48 |
+
}
|
49 |
+
pusher_stats {
|
50 |
+
uploaded_bytes: 2176
|
51 |
+
total_bytes: 2176
|
52 |
+
}
|
53 |
+
|
54 |
+
2022-05-23 10:05:59,811 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
55 |
+
wandb_count: 1
|
56 |
+
}
|
57 |
+
pusher_stats {
|
58 |
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uploaded_bytes: 2176
|
59 |
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total_bytes: 2176
|
60 |
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}
|
61 |
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|
62 |
+
2022-05-23 10:06:00,251 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
63 |
+
wandb_count: 5
|
64 |
+
}
|
65 |
+
pusher_stats {
|
66 |
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uploaded_bytes: 2176
|
67 |
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total_bytes: 1784001
|
68 |
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}
|
69 |
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|
70 |
+
2022-05-23 10:06:00,354 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
71 |
+
wandb_count: 5
|
72 |
+
}
|
73 |
+
pusher_stats {
|
74 |
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uploaded_bytes: 2176
|
75 |
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total_bytes: 1784001
|
76 |
+
}
|
77 |
+
|
78 |
+
2022-05-23 10:06:00,456 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
79 |
+
wandb_count: 5
|
80 |
+
}
|
81 |
+
pusher_stats {
|
82 |
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uploaded_bytes: 2176
|
83 |
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total_bytes: 1784001
|
84 |
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}
|
85 |
+
|
86 |
+
2022-05-23 10:06:00,558 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
87 |
+
wandb_count: 5
|
88 |
+
}
|
89 |
+
pusher_stats {
|
90 |
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uploaded_bytes: 2176
|
91 |
+
total_bytes: 1784001
|
92 |
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}
|
93 |
+
|
94 |
+
2022-05-23 10:06:00,661 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
95 |
+
wandb_count: 5
|
96 |
+
}
|
97 |
+
pusher_stats {
|
98 |
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uploaded_bytes: 1012683
|
99 |
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total_bytes: 1784001
|
100 |
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}
|
101 |
+
|
102 |
+
2022-05-23 10:06:00,763 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
103 |
+
wandb_count: 5
|
104 |
+
}
|
105 |
+
pusher_stats {
|
106 |
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uploaded_bytes: 1784001
|
107 |
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total_bytes: 1784001
|
108 |
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}
|
109 |
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|
110 |
+
2022-05-23 10:06:00,865 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
111 |
+
wandb_count: 5
|
112 |
+
}
|
113 |
+
pusher_stats {
|
114 |
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uploaded_bytes: 1784001
|
115 |
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total_bytes: 1784001
|
116 |
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}
|
117 |
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|
118 |
+
2022-05-23 10:06:00,968 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
119 |
+
wandb_count: 5
|
120 |
+
}
|
121 |
+
pusher_stats {
|
122 |
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uploaded_bytes: 1784001
|
123 |
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total_bytes: 1784001
|
124 |
+
}
|
125 |
+
|
126 |
+
2022-05-23 10:06:01,070 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
127 |
+
wandb_count: 5
|
128 |
+
}
|
129 |
+
pusher_stats {
|
130 |
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uploaded_bytes: 1784001
|
131 |
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total_bytes: 1784001
|
132 |
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}
|
133 |
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|
134 |
+
2022-05-23 10:06:01,173 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
135 |
+
wandb_count: 5
|
136 |
+
}
|
137 |
+
pusher_stats {
|
138 |
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uploaded_bytes: 1784001
|
139 |
+
total_bytes: 1784001
|
140 |
+
}
|
141 |
+
|
142 |
+
2022-05-23 10:06:01,275 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
143 |
+
wandb_count: 5
|
144 |
+
}
|
145 |
+
pusher_stats {
|
146 |
+
uploaded_bytes: 1784001
|
147 |
+
total_bytes: 1784001
|
148 |
+
}
|
149 |
+
|
150 |
+
2022-05-23 10:06:01,377 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
151 |
+
wandb_count: 5
|
152 |
+
}
|
153 |
+
pusher_stats {
|
154 |
+
uploaded_bytes: 1784001
|
155 |
+
total_bytes: 1784001
|
156 |
+
}
|
157 |
+
|
158 |
+
2022-05-23 10:06:01,480 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
159 |
+
wandb_count: 5
|
160 |
+
}
|
161 |
+
pusher_stats {
|
162 |
+
uploaded_bytes: 1784001
|
163 |
+
total_bytes: 1784001
|
164 |
+
}
|
165 |
+
|
166 |
+
2022-05-23 10:06:03,970 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
167 |
+
wandb_count: 5
|
168 |
+
}
|
169 |
+
pusher_stats {
|
170 |
+
uploaded_bytes: 1784001
|
171 |
+
total_bytes: 1784001
|
172 |
+
}
|
173 |
+
|
174 |
+
2022-05-23 10:06:04,516 INFO MainThread:1046672 [wandb_run.py:_on_finish():1995] got exit ret: done: true
|
175 |
+
exit_result {
|
176 |
+
}
|
177 |
+
file_counts {
|
178 |
+
wandb_count: 5
|
179 |
+
}
|
180 |
+
pusher_stats {
|
181 |
+
uploaded_bytes: 1784001
|
182 |
+
total_bytes: 1784001
|
183 |
+
}
|
184 |
+
local_info {
|
185 |
+
}
|
186 |
+
|
187 |
+
2022-05-23 10:06:05,688 INFO MainThread:1046672 [wandb_run.py:_footer_history_summary_info():3102] rendering history
|
188 |
+
2022-05-23 10:06:05,726 INFO MainThread:1046672 [wandb_run.py:_footer_history_summary_info():3134] rendering summary
|
189 |
+
2022-05-23 10:06:05,728 INFO MainThread:1046672 [wandb_run.py:_footer_sync_info():3057] logging synced files
|
wandb/run-20220523_091609-1iboydmy/run-1iboydmy.wandb
ADDED
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size 9201298
|
wandb/run-20220523_103002-wygrs7tw/files/config.yaml
ADDED
The diff for this file is too large to render.
See raw diff
|
|
wandb/run-20220523_103002-wygrs7tw/files/output.log
ADDED
@@ -0,0 +1,1746 @@
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0%|▏ | 500/503920 [19:41<127:22:04, 1.10it/s]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.
|
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***** Running Evaluation *****
|
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Num examples = 41040
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Batch size = 12
|
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{'loss': 2.9263, 'learning_rate': 4.9000000000000005e-06, 'epoch': 0.04}
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|
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|
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+
File "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py", line 819, in <module>█████████████████████████████████████████▏ | 3297/3420 [43:23<01:41, 1.21it/s]
|
1719 |
+
main()
|
1720 |
+
File "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py", line 770, in main
|
1721 |
+
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
1722 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 1497, in train
|
1723 |
+
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
|
1724 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 1624, in _maybe_log_save_evaluate
|
1725 |
+
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
|
1726 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 2284, in evaluate
|
1727 |
+
output = eval_loop(
|
1728 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/trainer.py", line 2448, in evaluation_loop
|
1729 |
+
for step, inputs in enumerate(dataloader):
|
1730 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 530, in __next__
|
1731 |
+
data = self._next_data()
|
1732 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 570, in _next_data
|
1733 |
+
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
|
1734 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
|
1735 |
+
return self.collate_fn(data)
|
1736 |
+
File "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py", line 281, in __call__
|
1737 |
+
batch = self.processor.pad(
|
1738 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py", line 82, in pad
|
1739 |
+
return self.current_processor.pad(*args, **kwargs)
|
1740 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/feature_extraction_sequence_utils.py", line 178, in pad
|
1741 |
+
processed_features[key] = [to_numpy(v) for v in value]
|
1742 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/feature_extraction_sequence_utils.py", line 178, in <listcomp>
|
1743 |
+
processed_features[key] = [to_numpy(v) for v in value]
|
1744 |
+
File "/mnt/lv_ai_1_dante/ml/rolvb/venv/lib/python3.9/site-packages/transformers/utils/generic.py", line 135, in to_numpy
|
1745 |
+
return np.array(obj)
|
1746 |
+
KeyboardInterrupt
|
wandb/run-20220523_103002-wygrs7tw/files/requirements.txt
ADDED
@@ -0,0 +1,77 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
aiohttp==3.8.1
|
2 |
+
aiosignal==1.2.0
|
3 |
+
appdirs==1.4.4
|
4 |
+
async-timeout==4.0.2
|
5 |
+
attrs==21.4.0
|
6 |
+
audioread==2.1.9
|
7 |
+
certifi==2021.10.8
|
8 |
+
cffi==1.15.0
|
9 |
+
charset-normalizer==2.0.12
|
10 |
+
click==8.1.2
|
11 |
+
datasets==2.1.0
|
12 |
+
decorator==5.1.1
|
13 |
+
dill==0.3.4
|
14 |
+
docker-pycreds==0.4.0
|
15 |
+
filelock==3.6.0
|
16 |
+
frozenlist==1.3.0
|
17 |
+
fsspec==2022.3.0
|
18 |
+
gitdb==4.0.9
|
19 |
+
gitpython==3.1.27
|
20 |
+
huggingface-hub==0.5.1
|
21 |
+
hypothesis==6.46.5
|
22 |
+
idna==3.3
|
23 |
+
jiwer==2.3.0
|
24 |
+
joblib==1.1.0
|
25 |
+
kenlm==0.0.0
|
26 |
+
librosa==0.9.1
|
27 |
+
llvmlite==0.38.0
|
28 |
+
multidict==6.0.2
|
29 |
+
multiprocess==0.70.12.2
|
30 |
+
numba==0.55.1
|
31 |
+
numpy==1.21.6
|
32 |
+
packaging==21.3
|
33 |
+
pandas==1.4.2
|
34 |
+
pathtools==0.1.2
|
35 |
+
pillow==9.1.0
|
36 |
+
pip==20.3.4
|
37 |
+
pkg-resources==0.0.0
|
38 |
+
pooch==1.6.0
|
39 |
+
promise==2.3
|
40 |
+
protobuf==3.20.1
|
41 |
+
psutil==5.9.0
|
42 |
+
pyarrow==7.0.0
|
43 |
+
pycparser==2.21
|
44 |
+
pyctcdecode==0.3.0
|
45 |
+
pygtrie==2.4.2
|
46 |
+
pyparsing==3.0.8
|
47 |
+
python-dateutil==2.8.2
|
48 |
+
python-levenshtein==0.12.2
|
49 |
+
pytz==2022.1
|
50 |
+
pyyaml==6.0
|
51 |
+
regex==2022.4.24
|
52 |
+
requests==2.27.1
|
53 |
+
resampy==0.2.2
|
54 |
+
responses==0.18.0
|
55 |
+
sacremoses==0.0.49
|
56 |
+
scikit-learn==1.0.2
|
57 |
+
scipy==1.8.0
|
58 |
+
sentry-sdk==1.5.10
|
59 |
+
setproctitle==1.2.3
|
60 |
+
setuptools==44.1.1
|
61 |
+
shortuuid==1.0.8
|
62 |
+
six==1.16.0
|
63 |
+
smmap==5.0.0
|
64 |
+
sortedcontainers==2.4.0
|
65 |
+
soundfile==0.10.3.post1
|
66 |
+
threadpoolctl==3.1.0
|
67 |
+
tokenizers==0.12.1
|
68 |
+
torch==1.11.0+cu113
|
69 |
+
torchaudio==0.11.0+cu113
|
70 |
+
torchvision==0.12.0+cu113
|
71 |
+
tqdm==4.64.0
|
72 |
+
transformers==4.18.0
|
73 |
+
typing-extensions==4.2.0
|
74 |
+
urllib3==1.26.9
|
75 |
+
wandb==0.12.15
|
76 |
+
xxhash==3.0.0
|
77 |
+
yarl==1.7.2
|
wandb/run-20220523_103002-wygrs7tw/files/wandb-metadata.json
ADDED
@@ -0,0 +1,62 @@
|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"os": "Linux-5.13.0-40-generic-x86_64-with-glibc2.34",
|
3 |
+
"python": "3.9.7",
|
4 |
+
"heartbeatAt": "2022-05-23T08:30:03.662767",
|
5 |
+
"startedAt": "2022-05-23T08:30:02.486763",
|
6 |
+
"docker": null,
|
7 |
+
"cpu_count": 96,
|
8 |
+
"cuda": null,
|
9 |
+
"args": [
|
10 |
+
"--model_name_or_path=facebook/wav2vec2-xls-r-1b",
|
11 |
+
"--hub_model_id=NbAiLab/wav2vec2-1b-npsc-nst-bokmaal",
|
12 |
+
"--output_dir=./",
|
13 |
+
"--overwrite_output_dir",
|
14 |
+
"--num_train_epochs=40",
|
15 |
+
"--per_device_train_batch_size=12",
|
16 |
+
"--per_device_eval_batch_size=12",
|
17 |
+
"--gradient_accumulation_steps=2",
|
18 |
+
"--learning_rate=2e-5",
|
19 |
+
"--warmup_steps=2000",
|
20 |
+
"--length_column_name=input_length",
|
21 |
+
"--evaluation_strategy=steps",
|
22 |
+
"--text_column_name=text",
|
23 |
+
"--save_steps=500",
|
24 |
+
"--eval_steps=500",
|
25 |
+
"--logging_steps=100",
|
26 |
+
"--layerdrop=0.041",
|
27 |
+
"--attention_dropout=0.094",
|
28 |
+
"--activation_dropout=0.055",
|
29 |
+
"--hidden_dropout=0.047",
|
30 |
+
"--save_total_limit=3",
|
31 |
+
"--freeze_feature_encoder",
|
32 |
+
"--feat_proj_dropout=0.04",
|
33 |
+
"--mask_time_prob=0.082",
|
34 |
+
"--mask_time_length=10",
|
35 |
+
"--mask_feature_prob=0.25",
|
36 |
+
"--mask_feature_length=64",
|
37 |
+
"--gradient_checkpointing",
|
38 |
+
"--min_duration_in_seconds=0.5",
|
39 |
+
"--max_duration_in_seconds=30.0",
|
40 |
+
"--use_auth_token",
|
41 |
+
"--seed=42",
|
42 |
+
"--fp16",
|
43 |
+
"--group_by_length",
|
44 |
+
"--do_train",
|
45 |
+
"--do_eval",
|
46 |
+
"--push_to_hub",
|
47 |
+
"--preprocessing_num_workers=32",
|
48 |
+
"--ctc_zero_infinity"
|
49 |
+
],
|
50 |
+
"state": "running",
|
51 |
+
"program": "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py",
|
52 |
+
"codePath": "run_speech_recognition_ctc.py",
|
53 |
+
"git": {
|
54 |
+
"remote": "https://huggingface.co/NbAiLab/wav2vec2-1b-npsc-nst-bokmaal",
|
55 |
+
"commit": "05ba5dd22c9cec7365fba75fae8630eec1302aff"
|
56 |
+
},
|
57 |
+
"email": "[email protected]",
|
58 |
+
"root": "/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal",
|
59 |
+
"host": "dante",
|
60 |
+
"username": "rolvb",
|
61 |
+
"executable": "/mnt/lv_ai_1_dante/ml/rolvb/venv/bin/python"
|
62 |
+
}
|
wandb/run-20220523_103002-wygrs7tw/files/wandb-summary.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
wandb/run-20220523_103002-wygrs7tw/logs/debug-internal.log
ADDED
The diff for this file is too large to render.
See raw diff
|
|
wandb/run-20220523_103002-wygrs7tw/logs/debug.log
ADDED
@@ -0,0 +1,181 @@
|
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1 |
+
2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_setup.py:_flush():75] Loading settings from /home/rolvb/.config/wandb/settings
|
2 |
+
2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_setup.py:_flush():75] Loading settings from /mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/wandb/settings
|
3 |
+
2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_setup.py:_flush():75] Loading settings from environment variables: {'project': 'wav2vec2', 'entity': 'NbAiLab'}
|
4 |
+
2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_setup.py:_flush():75] Inferring run settings from compute environment: {'program_relpath': 'run_speech_recognition_ctc.py', 'program': '/mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/run_speech_recognition_ctc.py'}
|
5 |
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2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_init.py:_log_setup():437] Logging user logs to /mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/wandb/run-20220523_103002-wygrs7tw/logs/debug.log
|
6 |
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2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_init.py:_log_setup():438] Logging internal logs to /mnt/lv_ai_1_dante/ml/models/wav2vec2-1b-npsc-nst-bokmaal/wandb/run-20220523_103002-wygrs7tw/logs/debug-internal.log
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7 |
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2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_init.py:init():471] calling init triggers
|
8 |
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2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_init.py:init():474] wandb.init called with sweep_config: {}
|
9 |
+
config: {}
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10 |
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2022-05-23 10:30:02,503 INFO MainThread:1047787 [wandb_init.py:init():524] starting backend
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11 |
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2022-05-23 10:30:02,503 INFO MainThread:1047787 [backend.py:_multiprocessing_setup():97] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
|
12 |
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2022-05-23 10:30:02,593 INFO MainThread:1047787 [backend.py:ensure_launched():217] starting backend process...
|
13 |
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2022-05-23 10:30:02,734 INFO MainThread:1047787 [backend.py:ensure_launched():222] started backend process with pid: 1048602
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14 |
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2022-05-23 10:30:02,736 INFO MainThread:1047787 [wandb_init.py:init():533] backend started and connected
|
15 |
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2022-05-23 10:30:02,745 INFO MainThread:1047787 [wandb_init.py:init():597] updated telemetry
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16 |
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2022-05-23 10:30:03,009 INFO MainThread:1047787 [wandb_init.py:init():628] communicating run to backend with 30 second timeout
|
17 |
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2022-05-23 10:30:03,518 INFO MainThread:1047787 [wandb_run.py:_on_init():1923] communicating current version
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18 |
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2022-05-23 10:30:03,647 INFO MainThread:1047787 [wandb_run.py:_on_init():1927] got version response upgrade_message: "wandb version 0.12.16 is available! To upgrade, please run:\n $ pip install wandb --upgrade"
|
19 |
+
|
20 |
+
2022-05-23 10:30:03,648 INFO MainThread:1047787 [wandb_init.py:init():659] starting run threads in backend
|
21 |
+
2022-05-23 10:30:03,698 INFO MainThread:1047787 [wandb_run.py:_console_start():1897] atexit reg
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22 |
+
2022-05-23 10:30:03,699 INFO MainThread:1047787 [wandb_run.py:_redirect():1770] redirect: SettingsConsole.REDIRECT
|
23 |
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2022-05-23 10:30:03,699 INFO MainThread:1047787 [wandb_run.py:_redirect():1775] Redirecting console.
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24 |
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2022-05-23 10:30:03,702 INFO MainThread:1047787 [wandb_run.py:_redirect():1831] Redirects installed.
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25 |
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2022-05-23 10:30:03,702 INFO MainThread:1047787 [wandb_init.py:init():684] run started, returning control to user process
|
26 |
+
2022-05-23 10:30:03,725 INFO MainThread:1047787 [wandb_run.py:_config_callback():1131] config_cb None None {'return_dict': True, 'output_hidden_states': False, 'output_attentions': False, 'torchscript': False, 'torch_dtype': 'float32', 'use_bfloat16': False, 'pruned_heads': {}, 'tie_word_embeddings': True, 'is_encoder_decoder': False, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 'tie_encoder_decoder': False, 'max_length': 20, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'chunk_size_feed_forward': 0, 'output_scores': False, 'return_dict_in_generate': False, 'forced_bos_token_id': None, 'forced_eos_token_id': None, 'remove_invalid_values': 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|
27 |
+
2022-05-23 10:30:03,728 INFO MainThread:1047787 [wandb_watch.py:watch():47] Watching
|
28 |
+
2022-05-23 11:33:10,501 INFO MainThread:1047787 [wandb_run.py:_atexit_cleanup():1866] got exitcode: 255
|
29 |
+
2022-05-23 11:33:10,503 INFO MainThread:1047787 [wandb_run.py:_restore():1838] restore
|
30 |
+
2022-05-23 11:33:12,930 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
31 |
+
wandb_count: 1
|
32 |
+
}
|
33 |
+
pusher_stats {
|
34 |
+
uploaded_bytes: 2176
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35 |
+
total_bytes: 2176
|
36 |
+
}
|
37 |
+
|
38 |
+
2022-05-23 11:33:13,179 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
39 |
+
wandb_count: 1
|
40 |
+
}
|
41 |
+
pusher_stats {
|
42 |
+
uploaded_bytes: 2176
|
43 |
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total_bytes: 2176
|
44 |
+
}
|
45 |
+
|
46 |
+
2022-05-23 11:33:15,044 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
47 |
+
wandb_count: 1
|
48 |
+
}
|
49 |
+
pusher_stats {
|
50 |
+
uploaded_bytes: 2176
|
51 |
+
total_bytes: 2176
|
52 |
+
}
|
53 |
+
|
54 |
+
2022-05-23 11:33:15,184 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
55 |
+
wandb_count: 5
|
56 |
+
}
|
57 |
+
pusher_stats {
|
58 |
+
uploaded_bytes: 2176
|
59 |
+
total_bytes: 1778293
|
60 |
+
}
|
61 |
+
|
62 |
+
2022-05-23 11:33:15,286 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
63 |
+
wandb_count: 5
|
64 |
+
}
|
65 |
+
pusher_stats {
|
66 |
+
uploaded_bytes: 2176
|
67 |
+
total_bytes: 1778293
|
68 |
+
}
|
69 |
+
|
70 |
+
2022-05-23 11:33:15,388 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
71 |
+
wandb_count: 5
|
72 |
+
}
|
73 |
+
pusher_stats {
|
74 |
+
uploaded_bytes: 2176
|
75 |
+
total_bytes: 1778293
|
76 |
+
}
|
77 |
+
|
78 |
+
2022-05-23 11:33:15,490 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
79 |
+
wandb_count: 5
|
80 |
+
}
|
81 |
+
pusher_stats {
|
82 |
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uploaded_bytes: 2176
|
83 |
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total_bytes: 1778293
|
84 |
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}
|
85 |
+
|
86 |
+
2022-05-23 11:33:15,593 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
87 |
+
wandb_count: 5
|
88 |
+
}
|
89 |
+
pusher_stats {
|
90 |
+
uploaded_bytes: 561382
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91 |
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total_bytes: 1778293
|
92 |
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}
|
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|
94 |
+
2022-05-23 11:33:15,695 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
95 |
+
wandb_count: 5
|
96 |
+
}
|
97 |
+
pusher_stats {
|
98 |
+
uploaded_bytes: 1778293
|
99 |
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total_bytes: 1778293
|
100 |
+
}
|
101 |
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|
102 |
+
2022-05-23 11:33:15,797 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
103 |
+
wandb_count: 5
|
104 |
+
}
|
105 |
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pusher_stats {
|
106 |
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uploaded_bytes: 1778293
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107 |
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total_bytes: 1778293
|
108 |
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}
|
109 |
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|
110 |
+
2022-05-23 11:33:15,899 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
111 |
+
wandb_count: 5
|
112 |
+
}
|
113 |
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pusher_stats {
|
114 |
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uploaded_bytes: 1778293
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total_bytes: 1778293
|
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}
|
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|
118 |
+
2022-05-23 11:33:16,001 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
119 |
+
wandb_count: 5
|
120 |
+
}
|
121 |
+
pusher_stats {
|
122 |
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uploaded_bytes: 1778293
|
123 |
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total_bytes: 1778293
|
124 |
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}
|
125 |
+
|
126 |
+
2022-05-23 11:33:16,103 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
127 |
+
wandb_count: 5
|
128 |
+
}
|
129 |
+
pusher_stats {
|
130 |
+
uploaded_bytes: 1778293
|
131 |
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total_bytes: 1778293
|
132 |
+
}
|
133 |
+
|
134 |
+
2022-05-23 11:33:16,206 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
135 |
+
wandb_count: 5
|
136 |
+
}
|
137 |
+
pusher_stats {
|
138 |
+
uploaded_bytes: 1778293
|
139 |
+
total_bytes: 1778293
|
140 |
+
}
|
141 |
+
|
142 |
+
2022-05-23 11:33:16,308 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
143 |
+
wandb_count: 5
|
144 |
+
}
|
145 |
+
pusher_stats {
|
146 |
+
uploaded_bytes: 1778293
|
147 |
+
total_bytes: 1778293
|
148 |
+
}
|
149 |
+
|
150 |
+
2022-05-23 11:33:16,410 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
151 |
+
wandb_count: 5
|
152 |
+
}
|
153 |
+
pusher_stats {
|
154 |
+
uploaded_bytes: 1778293
|
155 |
+
total_bytes: 1778293
|
156 |
+
}
|
157 |
+
|
158 |
+
2022-05-23 11:33:18,038 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: file_counts {
|
159 |
+
wandb_count: 5
|
160 |
+
}
|
161 |
+
pusher_stats {
|
162 |
+
uploaded_bytes: 1778293
|
163 |
+
total_bytes: 1778293
|
164 |
+
}
|
165 |
+
|
166 |
+
2022-05-23 11:33:18,596 INFO MainThread:1047787 [wandb_run.py:_on_finish():1995] got exit ret: done: true
|
167 |
+
exit_result {
|
168 |
+
}
|
169 |
+
file_counts {
|
170 |
+
wandb_count: 5
|
171 |
+
}
|
172 |
+
pusher_stats {
|
173 |
+
uploaded_bytes: 1778293
|
174 |
+
total_bytes: 1778293
|
175 |
+
}
|
176 |
+
local_info {
|
177 |
+
}
|
178 |
+
|
179 |
+
2022-05-23 11:33:19,771 INFO MainThread:1047787 [wandb_run.py:_footer_history_summary_info():3102] rendering history
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180 |
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2022-05-23 11:33:19,820 INFO MainThread:1047787 [wandb_run.py:_footer_history_summary_info():3134] rendering summary
|
181 |
+
2022-05-23 11:33:19,824 INFO MainThread:1047787 [wandb_run.py:_footer_sync_info():3057] logging synced files
|
wandb/run-20220523_103002-wygrs7tw/run-wygrs7tw.wandb
ADDED
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ADDED
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wandb/run-20220523_115145-3dybzmyz/files/output.log
ADDED
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0%| | 500/499680 [20:05<135:25:31, 1.02it/s]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`, you can safely ignore this message.
|
437 |
+
***** Running Evaluation *****
|
438 |
+
Num examples = 40740
|
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+
Batch size = 12
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
1784 |
+
|
1785 |
+
Configuration saved in ./checkpoint-500/config.json
|
1786 |
+
{'eval_loss': 2.797088623046875, 'eval_wer': 1.0, 'eval_runtime': 2782.5245, 'eval_samples_per_second': 14.641, 'eval_steps_per_second': 1.22, 'epoch': 0.04}
|
1787 |
+
Model weights saved in ./checkpoint-500/pytorch_model.bin
|
1788 |
+
Feature extractor saved in ./checkpoint-500/preprocessor_config.json
|
wandb/run-20220523_115145-3dybzmyz/files/requirements.txt
ADDED
@@ -0,0 +1,77 @@
|
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|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
1 |
+
aiohttp==3.8.1
|
2 |
+
aiosignal==1.2.0
|
3 |
+
appdirs==1.4.4
|
4 |
+
async-timeout==4.0.2
|
5 |
+
attrs==21.4.0
|
6 |
+
audioread==2.1.9
|
7 |
+
certifi==2021.10.8
|
8 |
+
cffi==1.15.0
|
9 |
+
charset-normalizer==2.0.12
|
10 |
+
click==8.1.2
|
11 |
+
datasets==2.1.0
|
12 |
+
decorator==5.1.1
|
13 |
+
dill==0.3.4
|
14 |
+
docker-pycreds==0.4.0
|
15 |
+
filelock==3.6.0
|
16 |
+
frozenlist==1.3.0
|
17 |
+
fsspec==2022.3.0
|
18 |
+
gitdb==4.0.9
|
19 |
+
gitpython==3.1.27
|
20 |
+
huggingface-hub==0.5.1
|
21 |
+
hypothesis==6.46.5
|
22 |
+
idna==3.3
|
23 |
+
jiwer==2.3.0
|
24 |
+
joblib==1.1.0
|
25 |
+
kenlm==0.0.0
|
26 |
+
librosa==0.9.1
|
27 |
+
llvmlite==0.38.0
|
28 |
+
multidict==6.0.2
|
29 |
+
multiprocess==0.70.12.2
|
30 |
+
numba==0.55.1
|
31 |
+
numpy==1.21.6
|
32 |
+
packaging==21.3
|
33 |
+
pandas==1.4.2
|
34 |
+
pathtools==0.1.2
|
35 |
+
pillow==9.1.0
|
36 |
+
pip==20.3.4
|
37 |
+
pkg-resources==0.0.0
|
38 |
+
pooch==1.6.0
|
39 |
+
promise==2.3
|
40 |
+
protobuf==3.20.1
|
41 |
+
psutil==5.9.0
|
42 |
+
pyarrow==7.0.0
|
43 |
+
pycparser==2.21
|
44 |
+
pyctcdecode==0.3.0
|
45 |
+
pygtrie==2.4.2
|
46 |
+
pyparsing==3.0.8
|
47 |
+
python-dateutil==2.8.2
|
48 |
+
python-levenshtein==0.12.2
|
49 |
+
pytz==2022.1
|
50 |
+
pyyaml==6.0
|
51 |
+
regex==2022.4.24
|
52 |
+
requests==2.27.1
|
53 |
+
resampy==0.2.2
|
54 |
+
responses==0.18.0
|
55 |
+
sacremoses==0.0.49
|
56 |
+
scikit-learn==1.0.2
|
57 |
+
scipy==1.8.0
|
58 |
+
sentry-sdk==1.5.10
|
59 |
+
setproctitle==1.2.3
|
60 |
+
setuptools==44.1.1
|
61 |
+
shortuuid==1.0.8
|
62 |
+
six==1.16.0
|
63 |
+
smmap==5.0.0
|
64 |
+
sortedcontainers==2.4.0
|
65 |
+
soundfile==0.10.3.post1
|
66 |
+
threadpoolctl==3.1.0
|
67 |
+
tokenizers==0.12.1
|
68 |
+
torch==1.11.0+cu113
|
69 |
+
torchaudio==0.11.0+cu113
|
70 |
+
torchvision==0.12.0+cu113
|
71 |
+
tqdm==4.64.0
|
72 |
+
transformers==4.18.0
|
73 |
+
typing-extensions==4.2.0
|
74 |
+
urllib3==1.26.9
|
75 |
+
wandb==0.12.15
|
76 |
+
xxhash==3.0.0
|
77 |
+
yarl==1.7.2
|
wandb/run-20220523_115145-3dybzmyz/files/wandb-metadata.json
ADDED
@@ -0,0 +1,62 @@
|
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|
|
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|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"os": "Linux-5.13.0-40-generic-x86_64-with-glibc2.34",
|
3 |
+
"python": "3.9.7",
|
4 |
+
"heartbeatAt": "2022-05-23T09:51:46.677597",
|
5 |
+
"startedAt": "2022-05-23T09:51:45.586535",
|
6 |
+
"docker": null,
|
7 |
+
"cpu_count": 96,
|
8 |
+
"cuda": null,
|
9 |
+
"args": [
|
10 |
+
"--model_name_or_path=facebook/wav2vec2-xls-r-1b",
|
11 |
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