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Model: Harveenchadha/vakyansh-wav2vec2-tamil-tam-250 Source: Original Platform
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README.md
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README.md
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---
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language: ta
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#datasets:
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#- Interspeech 2021
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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.
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81
config.json
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{
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"activation_dropout": 0.1,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForCTC"
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],
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"attention_dropout": 0.1,
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"bos_token": "<s>",
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"bos_token_id": 0,
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"codevector_dim": 256,
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"contrastive_logits_temperature": 0.1,
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"conv_bias": false,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2,
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2
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],
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"ctc_loss_reduction": "sum",
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"ctc_zero_infinity": false,
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"diversity_loss_weight": 0.1,
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"do_lower_case": false,
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"do_stable_layer_norm": false,
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"eos_token": "</s>",
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_norm": "group",
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"feat_proj_dropout": 0.1,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.1,
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"mask_feature_length": 10,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_prob": 0.05,
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"model_type": "wav2vec2",
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"num_attention_heads": 12,
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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 12,
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"num_negatives": 100,
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"pad_token": "[PAD]",
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"pad_token_id": 1,
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"proj_codevector_dim": 256,
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"torch_dtype": "float32",
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"transformers_version": "4.9.1",
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"unk_token": "[UNK]",
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"vocab_size": 53,
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"word_delimiter_token": "|"
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}
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preprocessor_config.json
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{
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"do_normalize": true,
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0,
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"return_attention_mask": false,
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"sampling_rate": 16000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:00adef7dc21c04f9014807f73c21c5fa12f9284aed6a5494a72bfc42ff3e55e7
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size 377731607
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
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{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "do_lower_case": false, "word_delimiter_token": "|", "tokenizer_class": "Wav2Vec2CTCTokenizer"}
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vocab.json
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{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3, "|": 4, "ஃ": 5, "அ": 6, "ஆ": 7, "இ": 8, "ஈ": 9, "உ": 10, "ஊ": 11, "எ": 12, "ஏ": 13, "ஐ": 14, "ஒ": 15, "ஓ": 16, "ஔ": 17, "க": 18, "ங": 19, "ச": 20, "ஜ": 21, "ஞ": 22, "ட": 23, "ண": 24, "த": 25, "ந": 26, "ன": 27, "ப": 28, "ம": 29, "ய": 30, "ர": 31, "ற": 32, "ல": 33, "ள": 34, "ழ": 35, "வ": 36, "ஶ": 37, "ஷ": 38, "ஸ": 39, "ஹ": 40, "ா": 41, "ி": 42, "ீ": 43, "ு": 44, "ூ": 45, "ெ": 46, "ே": 47, "ை": 48, "ொ": 49, "ோ": 50, "ௌ": 51, "்": 52}
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