Create README.md

README.md

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+---
+language: te
+datasets:
+- openslr
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: Anurag Singh XLSR Wav2Vec2 Large 53 Telugu
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: OpenSLR te
+      type: openslr
+      args: te
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 44.98
+---
+# Wav2Vec2-Large-XLSR-53-Telugu
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Telugu using the [OpenSLR SLR66](http://openslr.org/66/) 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:
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import pandas as pd
+
+# Evaluation notebook contains the procedure to download the data
+df = pd.read_csv("/content/te/test.tsv", sep="\t")
+df["path"] = "/content/te/clips/" + df["path"]
+test_dataset = Dataset.from_pandas(df)
+
+processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu")
+model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") 
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+# 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"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    return batch
+    
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["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["sentence"][:2])
+```
+## Evaluation
+```python
+import torch
+import torchaudio
+from datasets import Dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+from sklearn.model_selection import train_test_split
+import pandas as pd
+
+# Evaluation notebook contains the procedure to download the data
+df = pd.read_csv("/content/te/test.tsv", sep="\t")
+df["path"] = "/content/te/clips/" + df["path"]
+test_dataset = Dataset.from_pandas(df)
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu")
+model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") 
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\_\;\:\"\“\%\‘\”\।\’\'\&]'
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+def normalizer(text):
+    # Use your custom normalizer
+    text = text.replace("\\n","\n")
+    text = ' '.join(text.split())
+    text = re.sub(r'''([a-z]+)''','',text,flags=re.IGNORECASE)
+    text = re.sub(r'''%'''," శాతం ", text)
+    text = re.sub(r'''(/|-|_)'''," ", text)
+    text = re.sub("ై","ై", text)
+    text = text.strip()
+    return text
+
+def speech_file_to_array_fn(batch):
+    batch["sentence"] = normalizer(batch["sentence"])
+    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**: 44.98%
+## Training
+70% of the OpenSLR Marathi dataset was used for training.
+
+Train Split of annotations is [here](https://www.dropbox.com/s/xqc0wtour7f9h4c/train.tsv)
+
+Test Split of annotations is [here](https://www.dropbox.com/s/qw1uy63oj4qdiu4/test.tsv)
+
+Training Data Preparation notebook can be found [here](https://colab.research.google.com/drive/1_VR1QtY9qoiabyXBdJcOI29-xIKGdIzU?usp=sharing)
+
+Training notebook can be found[here](https://colab.research.google.com/drive/14N-j4m0Ng_oktPEBN5wiUhDDbyrKYt8I?usp=sharing)
+
+Evaluation notebook is [here](https://colab.research.google.com/drive/1SLEvbTWBwecIRTNqpQ0fFTqmr1-7MnSI?usp=sharing)