ayameRushia
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Delete eval.py
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eval.py
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import re
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import argparse
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import unicodedata
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from typing import Dict
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import torch
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import torchaudio
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from datasets import load_dataset, load_metric, Audio, Dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
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import re
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chars_to_ignore_regex = '[\é\!\,\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\’\—\–\·]'
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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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dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
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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["sentence"], predictions=result["pred_strings"])
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cer_result = cer.compute(references=result["sentence"], predictions=result["pred_strings"])
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# print & log results
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result_str = (
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f"WER: {wer_result}\n"
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f"CER: {cer_result}"
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)
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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["pred_strings"] + "\n")
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t.write(f"{i}" + "\n")
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t.write(batch["sentence"] + "\n")
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result.map(write_to_file, with_indices=True)
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def load_data(dataset_id, language, split='test'):
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test_dataset = load_dataset(dataset_id, language, split=split, use_auth_token=True)
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test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16_000))
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return test_dataset
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def speech_file_to_array_fn(batch):
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batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).lower() + " "
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batch["sentence"] = re.sub('!', '', batch["sentence"]).lower() + " "
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batch["sentence"] = batch["sentence"].replace('\"',"").replace("&","").replace("'","").replace("(","").lower() + " "
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batch["sentence"] = batch["sentence"].replace('[',"").replace("]","").replace("\\","").replace("«","").replace("»","").replace(")","").lower() + " "
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batch["sentence"] = batch["sentence"].replace(" "," ").replace(" "," ").replace(" "," ").lower() + " "
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batch["speech"] = batch["audio"]["array"]
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return batch
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def main(args):
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test_dataset = load_data(args.dataset, args.config, args.split)
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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model_id = args.model_id
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def evaluate_with_lm(batch):
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = model(**inputs.to('cuda')).logits
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int_result = processor.batch_decode(logits.cpu().numpy())
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batch["pred_strings"] = int_result.text
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return batch
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def evaluate(batch):
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = model(inputs.input_values.to('cuda')).logits
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pred_ids = torch.argmax(logits, dim=-1)
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batch["pred_strings"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
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return batch
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if args.lm:
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_id,use_auth_token=True)
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model = Wav2Vec2ForCTC.from_pretrained(model_id,use_auth_token=True)
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model.to('cuda')
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result = test_dataset.map(evaluate_with_lm, batched=True, batch_size=4)
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else:
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processor = Wav2Vec2Processor.from_pretrained(model_id,use_auth_token=True)
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model = Wav2Vec2ForCTC.from_pretrained(model_id,use_auth_token=True)
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model.to("cuda")
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result = test_dataset.map(evaluate, batched=True, batch_size=4)
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log_results(result, args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
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)
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parser.add_argument(
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"--dataset", type=str, required=True, help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets"
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)
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parser.add_argument(
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"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
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)
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parser.add_argument(
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"--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`"
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)
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parser.add_argument(
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"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to None. For long audio files a good value would be 5.0 seconds."
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)
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parser.add_argument(
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"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to None. For long audio files a good value would be 1.0 seconds."
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)
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parser.add_argument(
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"--log_outputs", action='store_true', help="If defined, write outputs to log file for analysis."
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)
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parser.add_argument(
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"--lm", action='store_true', help="Using language model for evaluation or not."
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)
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args = parser.parse_args()
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main(args)
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