distil-large

Distil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling.

This is the third and final installment of the Distil-Whisper English series. It the knowledge distilled version of OpenAI's Whisper large-v3, the latest and most performant Whisper model to date.

Compared to previous Distil-Whisper models, the distillation procedure for distil-large-v3 has been adapted to give superior long-form transcription accuracy with OpenAI's sequential long-form algorithm.

The result is a distilled model that performs to within 1% WER of large-v3 on long-form audio using both the sequential and chunked algorithms, and outperforms distil-large-v2 by 4.8% using the sequential algorithm. The model is also faster than previous Distil-Whisper models: 6.3x faster than large-v3, and 1.1x faster than distil-large-v2.

Since the sequential algorithm is the "de-facto" transcription algorithm across the most popular Whisper libraries (Whisper cpp, Faster-Whisper, OpenAI Whisper), this distilled model is designed to be compatible with these libraries. You can expect significant performance gains by switching from previous Distil-Whisper checkpoints to distil-large-v3 when using these libraries. For convenience, the weights for the most popular libraries are already converted, with instructions for getting started below.

Table of Contents

1. Transformers Usage
   • Short-Form Transcription
   • Sequential Long-Form
   • Chunked Long-Form
   • Speculative Decoding
   • Additional Speed and Memory Improvements
2. Library Integrations
   • Whisper cpp
   • Faster Whisper
   • OpenAI Whisper
   • Transformers.js
   • Candle
3. Model Details
4. License

Transformers Usage

distil-large-v3 is supported in the Hugging Face 🤗 Transformers library from version 4.39 onwards. To run the model, first install the latest version of Transformers. For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub:

pip install --upgrade pip
pip install --upgrade transformers accelerate datasets[audio]

Short-Form Transcription

The model can be used with the pipeline class to transcribe short-form audio files (< 30-seconds) as follows:

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "distil-whisper/distil-large-v3"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    torch_dtype=torch_dtype,
    device=device,
)

dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[0]["audio"]

result = pipe(sample)
print(result["text"])

To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:

- result = pipe(sample)
+ result = pipe("audio.mp3")

For segment-level timestamps, pass the argument return_timestamps=True and return the "chunks" output:

result = pipe(sample, return_timestamps=True)
print(result["chunks"])

Ad-hoc generation arguments can be passed to model.generate, including num_beams for beam-search, return_timestamps for segment-level timestamps, and prompt_ids for prompting. See the docstrings for more details.

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from datasets import Audio, load_dataset

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "distil-whisper/distil-large-v3"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
sample = dataset[0]["audio"]

input_features = processor(
  sample["array"], samplin