Create README.md

README.md

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+---
+language: mr
+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 Marathi by Sumedh Khodke
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: OpenSLR mr
+      type: openslr
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 12.7
+---
+
+# Wav2Vec2-Large-XLSR-53-Marathi
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices, although it works well for male voice too.
+## Usage
+The model can be used directly (without a language model) as follows, given that you have a dataset with Marathi `actual_text` and `path_in_folder` columns:
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+#test_dataset = load_dataset("common_voice", "{lang_id}", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
+mr_test_dataset_new = all_data['test']
+
+processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") 
+model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") 
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000) #first arg - input sample, second arg - output sample
+# Preprocessing the datasets. We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+	speech_array, sampling_rate = torchaudio.load(batch["path_in_folder"])
+	batch["speech"] = resampler(speech_array).squeeze().numpy()
+	return batch
+mr_test_dataset_new = mr_test_dataset_new.map(speech_file_to_array_fn)
+inputs = processor(mr_test_dataset_new["speech"][:5], 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:", mr_test_dataset_new["actual_text"][:5])
+```
+## Evaluation
+Evaluated on 10% of the Marathi data on Open SLR-64.
+```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", "{lang_id}", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
+mr_test_dataset_new = all_data['test']
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi")
+model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") 
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' 
+resampler = torchaudio.transforms.Resample(48_000, 16_000) #first arg - input sample, second arg - output sample
+# Preprocessing the datasets. We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+	batch["actual_text"] = re.sub(chars_to_ignore_regex, '', batch["actual_text"]).lower()
+	speech_array, sampling_rate = torchaudio.load(batch["path_in_folder"])
+	batch["speech"] = resampler(speech_array).squeeze().numpy()
+	return batch
+mr_test_dataset_new = mr_test_dataset_new.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 = mr_test_dataset_new.map(evaluate, batched=True, batch_size=8)
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["actual_text"])))
+```
+**WER on the Test Set**: 12.70 %  
+## Training
+90% of the OpenSLR Marathi dataset was used for training.
+The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1wX46fjExcgU5t3AsWhSPTipWg_aMDg2f?usp=sharing).