Created readme.md

README.md

@@ -0,0 +1,58 @@
+---
+language: Bengali
+datasets:
+- OpenSLR
+metrics:
+- wer
+tags:
+- bn
+- audio
+- automatic-speech-recognition
+- speech
+license: cc-by-sa-4.0
+model-index:
+- name: XLSR Wav2Vec2 Bengali by Arijit
+  results:
+  - task:
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: OpenSLR
+      type: OpenSLR
+      args: ben
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 32.45
+---
+# Wav2Vec2-Large-XLSR-Bengali
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using a subset of 40,000 utterances from [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/). Tested WER using ~4200 held out from training.
+When using this model, make sure that your speech input is sampled at 16kHz.
+Train Script can be Found at : train.py 
+    Data Prep Notebook : https://colab.research.google.com/drive/1JMlZPU-DrezXjZ2t7sOVqn7CJjZhdK2q?usp=sharing
+    Inference Notebook : https://colab.research.google.com/drive/1uKC2cK9JfUPDTUHbrNdOYqKtNozhxqgZ?usp=sharing
+## Usage
+
+The model can be used directly (without a language model) as follows:
+```python
+import torch
+import torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+processor = Wav2Vec2Processor.from_pretrained("arijitx/wav2vec2-large-xlsr-bengali")
+model = Wav2Vec2ForCTC.from_pretrained("arijitx/wav2vec2-large-xlsr-bengali")
+# model = model.to("cuda")
+resampler = torchaudio.transforms.Resample(TEST_AUDIO_SR, 16_000)
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch)
+    speech =  resampler(speech_array).squeeze().numpy()
+    return speech
+speech_array = speech_file_to_array_fn("test_file.wav")
+inputs = processor(speech_array, sampling_rate=16_000, return_tensors="pt", padding=True)
+with torch.no_grad():
+    logits = model(inputs.input_values).logits
+    
+predicted_ids = torch.argmax(logits, dim=-1)
+preds = processor.batch_decode(predicted_ids)[0]
+print(preds.replace("[PAD]",""))
+```
+**Test Result**: WER on ~4200 utterance : 32.45 %