Wav2vec2-Large-XLSR-Tamil

Wav2Vec2-Large-XLSR-Tamil

When using this model, make sure that your speech input is sampled at 16kHz.

Inference

The model can be used directly as follows:

!pip install datasets 
!pip install transformers 

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

import torch
import librosa
from datasets import load_dataset

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

processor = Wav2Vec2Processor.from_pretrained("Gobee/Wav2vec2-Large-XLSR-Tamil")
model = Wav2Vec2ForCTC.from_pretrained("Gobee/Wav2vec2-Large-XLSR-Tamil")

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

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    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 {language} test data of Common Voice.

!pip install datasets 
!pip install transformers 
!pip install jiwer

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

import torch
import librosa
from datasets import load_dataset, load_metric
import re

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

processor = Wav2Vec2Processor.from_pretrained("Gobee/Wav2vec2-Large-XLSR-Tamil")
model = Wav2Vec2ForCTC.from_pretrained("Gobee/Wav2vec2-Large-XLSR-Tamil")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\ \’\–\(\)]'

# 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 = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    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: 57.004356 %

Usage and Evaluation script

The script used for usage and evaluation can be found here

Training

The Common Voice train, validation datasets were used for training.

The script used for training can be found here