license: mit
datasets:
- openslr/librispeech_asr
tags:
- ASR
- Automatic Speech Recognition
- Whisper
- Medusa
- Speech
- Speculative Decoding
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-v1 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 whisper-medusa-v1 install whisper-medusa repo following the README instructions.
Inference can be done using the following code:
import torch
import torchaudio
from whisper_medusa import WhisperMedusaModel
from transformers import WhisperProcessor
model_name = "aiola/whisper-medusa-v1"
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)