Update README.md

README.md

@@ -1,3 +1,70 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- openslr/librispeech_asr
+tags:
+- ASR
+- Automatic Speech Recognition
+- Whisper
+- Medusa
+- Speech
+- Speculative Decoding
+language:
+- en
+---
+
+# Whisper Medusa
+
+Whisper is an advanced encoder-decoder model for speech transcription and 
+translation, processing audio through encoding and decoding stages. Given 
+its large size and slow inference speed, various optimization strategies like 
+Faster-Whisper and Speculative Decoding have been proposed to enhance performance. 
+Our Medusa model builds on Whisper by predicting multiple tokens per iteration, 
+which significantly improves speed with small degradation in WER. We train and 
+evaluate our model on the LibriSpeech dataset, demonstrating speed improvements.
+
+---------
+
+## Training Details
+`aiola/whisper-medusa-block-libri` was trained on the LibriSpeech dataset to perform audio translation. 
+The Medusa heads were optimized for English, so for optimal performance and speed improvements, please use English audio only. 
+
+---------
+
+## Usage
+To use `aiola/whisper-medusa-block-libri` install [`whisper-medusa`](https://github.com/aiola-lab/whisper-medusa) repo following the README instructions.
+
+Inference can be done using the following code:
+```python
+import torch
+import torchaudio
+
+from whisper_medusa import WhisperMedusaModel
+from transformers import WhisperProcessor
+
+model_name = "aiola/whisper-medusa-block-libri"
+model = WhisperMedusaModel.from_pretrained(model_name)
+processor = WhisperProcessor.from_pretrained(model_name)
+
+path_to_audio = "path/to/audio.wav"
+SAMPLING_RATE = 16000
+language = "en"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+input_speech, sr = torchaudio.load(path_to_audio)
+if sr != SAMPLING_RATE:
+    input_speech = torchaudio.transforms.Resample(sr, SAMPLING_RATE)(input_speech)
+
+input_features = processor(input_speech.squeeze(), return_tensors="pt", sampling_rate=SAMPLING_RATE).input_features
+input_features = input_features.to(device)
+
+model = model.to(device)
+model_output = model.generate(
+    input_features,
+    language=language,
+)
+predict_ids = model_output[0]
+pred = processor.decode(predict_ids, skip_special_tokens=True)
+print(pred)
+
+```