Update README.md

README.md

@@ -21,4 +21,53 @@ widget:
   src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
 - example_title: Librispeech sample 2
   src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
----
+---
+## Description
+This model is a distilled version of the Whisper large v2 model using decoder pruning. 
+It is trained to give the same distribution as the teacher(large-v2) model using Distillation loss (KL loss) + CE Loss. 
+The original model contains 32 decoder layers, whereas the distilled model contains only 8 layers and achieves 4.2% WER on the 
+librispeech dataset with finetuning for just one epoch. The decoding speed of the model is 2x faster than vanilla large-v2 and 
+40% smaller in size.
+
+## Train on your data
+```shell
+accelerate launch student-teacher-distillation-streaming.py --freeze_encoder --keep_punctuation 
+--keep_case --teacher_model_name_or_path openai/whisper-large-v2 --student_model_name_or_path large-v2-2 
+--student_cache_dir large-v2-2 --output_dir whisper-large-v2-2-en-cv --data_cache_dir commonvoice 
+--teacher_cache_dir cache --student_cache_dir large-v2-2-en-cv --text_column sentence 
+--train_dataset_name mozilla-foundation/common_voice_13_0 --train_dataset_config_name en --train_split_name train 
+--validation_dataset_name mozilla-foundation/common_voice_13_0 --validation_dataset_config_name en 
+--validation_split_name test --max_val_samples 2000
+```
+
+## Inference
+```python
+>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
+>>> from datasets import load_dataset
+
+>>> # load model and processor
+>>> processor = WhisperProcessor.from_pretrained("rsonavane/distil-whisper-large-v2-8-ls")
+>>> model = WhisperForConditionalGeneration.from_pretrained("rsonavane/distil-whisper-large-v2-8-ls")
+>>> model.config.forced_decoder_ids = None
+
+>>> # load dummy dataset and read audio files
+>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+>>> sample = ds[0]["audio"]
+>>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features 
+
+>>> # generate token ids
+>>> predicted_ids = model.generate(input_features)
+>>> # decode token ids to text
+>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
+['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>']
+
+>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']
+```
+
+## Limitations
+This experiment aimed to explore the effectiveness of decoder pruning and distillation in enhancing performance after training.
+The model acquires a similar internal representation of the English language as its teacher model, 
+but with improved inference speed and efficiency for downstream tasks. Additionally, it can be fine-tuned for multiple languages, 
+maintaining the original model's performance while reducing inference latency. 
+There are other frameworks such as JAX that can help improve the same.