Create README.md

README.md

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+---
+language: gu
+datasets:
+- openslr
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: XLSR Wav2Vec2 Large 53 Gujarati by Gunjan Chhablani
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: OpenSLR gu
+      type: openslr
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 23.55
+---
+
+# Wav2Vec2-Large-XLSR-53-Gujarati
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Gujarati using the [OpenSLR SLR78](http://openslr.org/78/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. 
+
+## Usage
+
+The model can be used directly (without a language model) as follows, assuming you have a dataset with Gujarati `sentence` and `path` fields:
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+# test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. 
+# For sample see the Colab link in Training Section.
+
+processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu")
+model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
+
+# 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_eval = test_dataset_eval.map(speech_file_to_array_fn)
+inputs = processor(test_dataset_eval["speech"][:2], 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_eval["sentence"][:2])
+```
+
+
+## Evaluation
+
+The model can be evaluated as follows on 10% of the Marathi data on OpenSLR.
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+# test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.
+
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu")
+model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu")
+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)
+
+# 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"), 
+        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"])))
+```
+
+**Test Result**: 14.53 %  
+
+## Training
+
+90% of the OpenSLR Gujarati Male+Female dataset was used for training, after removing few examples that contained Roman characters.
+The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1fRQlgl4EPR4qKGScgza3MpWgbL5BeWtn?usp=sharing).