Wav2Vec2-Large-XLSR-Bengali

Fine-tuned facebook/wav2vec2-large-xlsr-53 Bengali using the Bengali ASR training data set containing ~196K utterances. When using this model, make sure that your speech input is sampled at 16kHz.

Usage

Dataset must be downloaded from this website and preprocessed accordingly. For example 1250 test samples has been chosen.

import pandas as pd
test_dataset = pd.read_csv('utt_spk_text.tsv', sep='\\t', header=None)[60000:61250]
test_dataset.columns = ["audio_path", "__", "label"]
test_dataset = test_data.drop("__", axis=1)
def add_file_path(text):
  path = "data/" + text[:2] + "/" + text + '.flac'
  return path
test_dataset['audio_path'] = test_dataset['audio_path'].map(lambda x: add_file_path(x))

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

import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
processor = Wav2Vec2Processor.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
model = Wav2Vec2ForCTC.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
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["audio_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["label"][:2])

Evaluation

The model can be evaluated as follows on the Bengali test data of OpenSLR.

import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
model = Wav2Vec2ForCTC.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
model.to("cuda")
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["label"]).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: 88.58 %

Training

The script used for training can be found Bengali ASR Fine Tuning Wav2Vec2