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aiola/
whisper-medusa-block-libri

Name: whisper-medusa-block-libri
Availability: InStock

safetensors

whisper

ASR

Automatic Speech Recognition

openslr/librispeech_asr

mit

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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 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)