README.md

metadata

language: cnh
datasets:
  - common_voice
metrics:
  - wer
tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
license: apache-2.0
model-index:
  - name: Wav2Vec2 Large 53 Hakha Chin by Gunjan Chhablani
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice cnh
          type: common_voice
          args: cnh
        metrics:
          - name: Test WER
            type: wer
            value: 31.38

Wav2Vec2-Large-XLSR-53-Hakha-Chin

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hakha Chin using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.

Usage

The model can be used directly (without a language model) as follows:

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "cnh", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh")
model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh/")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  batch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])

Evaluation

The model can be evaluated as follows on the Portuguese test data of Common Voice.

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "cnh", split="test")
wer = load_metric("wer")


processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh")
model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\/]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  batch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
  inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

  with torch.no_grad():
    logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
    pred_ids = torch.argmax(logits, dim=-1)

  batch["pred_strings"] = processor.batch_decode(pred_ids)
  return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))

Test Result: 31.38 %

Training

The Common Voice train and validation datasets were used for training. The script used for training can be found here. The parameters passed were:

#!/usr/bin/env bash
python run_common_voice.py \\
    --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \\
    --dataset_config_name="pt" \\
    --output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \\
    --cache_dir=/workspace/data \\
    --overwrite_output_dir \\
    --num_train_epochs="30" \\
    --per_device_train_batch_size="32" \\
    --per_device_eval_batch_size="32" \\
    --evaluation_strategy="steps" \\
    --learning_rate="3e-4" \\
    --warmup_steps="500" \\
    --fp16 \\
    --freeze_feature_extractor \\
    --save_steps="500" \\
    --eval_steps="500" \\
    --save_total_limit="1" \\
    --logging_steps="500" \\
    --group_by_length \\
    --feat_proj_dropout="0.0" \\
    --layerdrop="0.1" \\
    --gradient_checkpointing \\
    --do_train --do_eval \\

Notebook containing the evaluation can be found here.