First readme here!

README.md

@@ -0,0 +1,133 @@
+---
+language: hi
+datasets:
+- openslr_hindi
+- common_voice
+metrics:
+- wer 
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week 
+- xlsr-hindi
+license: apache-2.0 
+model-index:
+- name: Fine-tuned Hindi XLSR Wav2Vec2 Large 
+  results:
+  - task:
+      name: Speech Recognition 
+      type: automatic-speech-recognition 
+    datasets: 
+      - name: Common Voice hi 
+        type: common_voice 
+        args: hi
+      - name: OpenSLR Hindi
+        url: https://www.openslr.org/resources/103/
+    metrics:
+      - name: Test WER 
+        type: wer 
+        value: 46.05 
+---
+
+# Wav2Vec2-Large-XLSR-Hindi
+
+Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi using OpenSLR Hindi dataset for training and Common Voice Hindi Test dataset for Evaluation. The OpenSLR Hindi data used for training was of size 10000 and it was randomly sampled. The OpenSLR train and test sets were combined and used as training data in order to increase the amount of variations. The evaluation was done on Common Voice Test set. The OpenSLR data is 8kHz and hence it was upsampled to 16kHz for training. 
+
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+*Note: This is the first iteration of the fine-tuning. Will update this model if WER improves in future experiments.*
+
+## Test Results
+
+| Dataset | WER |
+| ------- | --- |
+| Test split Common Voice Hindi | 46.055 % |
+
+
+## Usage 
+
+The model can be used directly (without a language model) as follows:
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+test_dataset = load_dataset("common_voice", "hi", split="test[:2%]")
+
+processor = Wav2Vec2Processor.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi") 
+model = Wav2Vec2ForCTC.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")
+
+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 = torchaudio.load(batch["path"])
+	batch["speech"] = resampler(speech_array).squeeze().numpy()
+	return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
+```
+
+## 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", "hi", split="test") 
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi") 
+model = Wav2Vec2ForCTC.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi") 
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\�\।\']'
+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):
+	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)
+
+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"])))
+```
+
+## Code
+
+The Notebook used for training this model can be found at [shiwangi27/googlecolab](https://github.com/shiwangi27/googlecolab/blob/main/run_common_voice.ipynb). 
+I used a modified version of [run_common_voice.py](https://github.com/shiwangi27/googlecolab/blob/main/run_common_voice.py) for training.