Update README.md

README.md

@@ -8,9 +8,11 @@ tags:
 - audio
 - automatic-speech-recognition
 - speech
+- hindi asr
+- hindi speech recognition
 license: mit
 model-index:
-- name: Wav2Vec2 Vakyansh Hindi Model by Harveen Chadha
+- name: Wav2Vec2 Vakyansh Hindi Model by Yuvraj Mor
   results:
   - task:
       name: Speech Recognition
@@ -23,116 +25,4 @@ model-index:
     - name: Test WER
       type: wer
       value: 33.17
----
-
-## Spaces Demo
-Check the spaces demo [here](https://huggingface.co/spaces/Harveenchadha/wav2vec2-vakyansh-hindi/tree/main)
-
-## Pretrained Model
-
-Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz.
-
-**Note: The result from this model is without a language model so you may witness a higher WER in some cases.**
-
-## Dataset
-
-This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.
-
-## Training Script
-
-Models were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the [training repository](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation).
-
-In case you want to explore training logs on wandb they are [here](https://wandb.ai/harveenchadha/hindi_finetuning_multilingual?workspace=user-harveenchadha).
-
-
-## [Colab Demo](https://colab.research.google.com/github/harveenchadha/bol/blob/main/demos/hf/hindi/hf_hindi_him_4200_demo.ipynb)
-
-## Usage
-
-The model can be used directly (without a language model) as follows:
-
-```python
-import soundfile as sf
-import torch
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-import argparse
-
-def parse_transcription(wav_file):
-    # load pretrained model
-    processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
-    model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
-
-    # load audio
-    audio_input, sample_rate = sf.read(wav_file)
-
-    # pad input values and return pt tensor
-    input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
-
-    # INFERENCE
-    # retrieve logits & take argmax
-    logits = model(input_values).logits
-    predicted_ids = torch.argmax(logits, dim=-1)
-
-    # transcribe
-    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
-    print(transcription)
-
-```
-
-
-## 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("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
-model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
-model.to("cuda")
-
-resampler = torchaudio.transforms.Resample(48_000, 16_000)
-
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
-
-# Preprocessing the datasets.
-# We need to read the aduio 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")).logits
-
-      pred_ids = torch.argmax(logits, dim=-1)
-      batch["pred_strings"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
-      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**: 33.17 %
-
-[**Colab Evaluation**](https://colab.research.google.com/github/harveenchadha/bol/blob/main/demos/hf/hindi/hf_vakyansh_hindi_him_4200_evaluation_common_voice.ipynb) 
-
-## Credits
-Thanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages.
+---