updated

README.md

@@ -22,3 +22,54 @@ model-index:
       type: wer
       value: 24.17
 ---
+
+# hindi-clsril-100
+
+Fine-tuned [Harveenchadha/wav2vec2-pretrained-clsril-23-10k](https://huggingface.co/Harveenchadha/wav2vec2-pretrained-clsril-23-10k) on Hindi using the [Common Voice](https://huggingface.co/datasets/common_voice), included [openSLR](http://www.openslr.org/103/) Hindi dataset. 
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+## Evaluation
+The model can be used directly (with or without a language model) as follows:
+
+```python
+#!pip install datasets==1.4.1
+#!pip install transformers==4.4.0
+#!pip install torchaudio
+#!pip install jiwer
+
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM
+
+test_dataset = load_dataset("common_voice", "hi", split="test") 
+wer = load_metric("wer")
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\�\।\']'
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+# 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_with_lm(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
+    batch["pred_strings"] = transcription = processor_with_lm.batch_decode(logits.numpy()).text
+    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**: 24.17 %
+
+## Training
+
+The Common Voice hi `train`, `validation` were used for training, as well as openSLR hi `train`, `validation` and `test` datasets.
+
+The script used for training can be found here [colab](https://colab.research.google.com/drive/1YL_csb3LRjqWybeyvQhZ-Hem2dtpvq_x?usp=sharing)