Create README.md

README.md

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+---
+language: ta
+#datasets:
+#- Interspeech 2021
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+license: MIT
+model-index:
+- name: Wav2Vec2 Vakyansh Tamil Model by Harveen Chadha
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice ta
+      type: common_voice
+      args: ta  
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 33.17
+---
+
+## Pretrained Model
+
+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.
+
+**Note: The result from this model is without a language model so you may witness a higher WER in some cases.**
+
+## Dataset
+
+This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.
+
+## Training Script
+
+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).
+
+In case you want to explore training logs on wandb they are [here](https://wandb.ai/harveenchadha/hindi_finetuning_multilingual?workspace=user-harveenchadha).
+
+
+## [Colab Demo](https://colab.research.google.com/github/harveenchadha/bol/blob/main/demos/hf/hindi/hf_hindi_him_4200_demo.ipynb)
+
+## Usage
+
+The model can be used directly (without a language model) as follows:
+
+```python
+import soundfile as sf
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import argparse
+
+def parse_transcription(wav_file):
+    # load pretrained model
+    processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
+    model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
+
+    # load audio
+    audio_input, sample_rate = sf.read(wav_file)
+
+    # pad input values and return pt tensor
+    input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
+
+    # INFERENCE
+    # retrieve logits & take argmax
+    logits = model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+
+    # transcribe
+    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+    print(transcription)
+
+```
+
+
+## Evaluation
+The model can be evaluated as follows on the hindi test data of Common Voice. 
+
+```python
+
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+test_dataset = load_dataset("common_voice", "ta", split="test")
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
+model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
+model.to("cuda")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+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 = 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")).logits
+
+      pred_ids = torch.argmax(logits, dim=-1)
+      batch["pred_strings"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
+      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**: 53.64 %
+
+[**Colab Evaluation**](https://colab.research.google.com/github/harveenchadha/bol/blob/main/demos/hf/hindi/hf_vakyansh_hindi_him_4200_evaluation_common_voice.ipynb) 
+
+## Credits
+Thanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages.