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--- |
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language: ta |
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metrics: |
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- wer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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license: mit |
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model-index: |
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- name: Wav2Vec2 Vakyansh Tamil Model by Harveen Chadha |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice ta |
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type: common_voice |
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args: ta |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 53.64 |
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--- |
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## Pretrained Model |
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Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz. |
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**Note: The result from this model is without a language model so you may witness a higher WER in some cases.** |
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## Dataset |
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This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now. |
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## Training Script |
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Models were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the [training repository](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation). |
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In case you want to explore training logs on wandb they are [here](https://wandb.ai/harveenchadha/tamil-finetuning-multilingual). |
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## [Colab Demo](https://github.com/harveenchadha/bol/blob/main/demos/hf/tamil/hf_tamil_tnm_4200_demo.ipynb) |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import soundfile as sf |
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import torch |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import argparse |
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def parse_transcription(wav_file): |
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# load pretrained model |
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processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250") |
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model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250") |
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# load audio |
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audio_input, sample_rate = sf.read(wav_file) |
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# pad input values and return pt tensor |
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input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values |
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# INFERENCE |
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# retrieve logits & take argmax |
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logits = model(input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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# transcribe |
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transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) |
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print(transcription) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the hindi test data of Common Voice. |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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test_dataset = load_dataset("common_voice", "ta", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250") |
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model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250") |
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model.to("cuda") |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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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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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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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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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result**: 53.64 % |
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[**Colab Evaluation**](https://github.com/harveenchadha/bol/blob/main/demos/hf/tamil/hf_vakyansh_tamil_tnm_4200_evaluation_common_voice.ipynb) |
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## Credits |
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Thanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages. |
