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Distil-Whisper: distil-large-v2

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

It is a distilled version of the Whisper model that is 6 times faster, 49% smaller, and performs within 1% WER on out-of-distribution evaluation sets. This is the repository for distil-large-v2, a distilled variant of Whisper large-v2.

Model Params / M Rel. Latency ↑ Short-Form WER ↓ Long-Form WER ↓
large-v2 1550 1.0 9.1 11.7
distil-large-v2 756 5.8 10.1 11.6
distil-medium.en 394 6.8 11.1 12.4
distil-small.en 166 5.6 12.1 12.8

Note: Distil-Whisper is currently only available for English speech recognition. We are working with the community to distill Whisper on other languages. If you are interested in distilling Whisper in your language, check out the provided training code. We will update the Distil-Whisper repository with multilingual checkpoints when ready!

Usage

Distil-Whisper is supported in Hugging Face πŸ€— Transformers from version 4.35 onwards. To run the model, first install the latest version of the Transformers library. For this example, we'll also install πŸ€— Datasets to load 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-v2"

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")

Long-Form Transcription

Distil-Whisper uses a chunked algorithm to transcribe long-form audio files (> 30-seconds). In practice, this chunked long-form algorithm is 9x faster than the sequential algorithm proposed by OpenAI in the Whisper paper (see Table 7 of the Distil-Whisper paper).

To enable chunking, pass the chunk_length_s parameter to the pipeline. For Distil-Whisper, a chunk length of 15-seconds is optimal. To activate batching, pass the argument batch_size:

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-v2"

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,
    chunk_length_s=15,
    batch_size=16,
    torch_dtype=torch_dtype,
    device=device,
)

dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
sample = dataset[0]["audio"]

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

Speculative Decoding

Distil-Whisper can be used as an assistant model to Whisper for speculative decoding. Speculative decoding mathematically ensures the exact same outputs as Whisper are obtained while being 2 times faster. This makes it the perfect drop-in replacement for existing Whisper pipelines, since the same outputs are guaranteed.

In the following code-snippet, we load the assistant Distil-Whisper model standalone to the main Whisper pipeline. We then specify it as the "assistant model" for generation:

from transformers import pipeline, AutoModelForCausalLM, AutoModelForSpeechSeq2Seq, AutoProcessor
import torch
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

assistant_model_id = "distil-whisper/distil-large-v2"

assistant_model = AutoModelForCausalLM.from_pretrained(
    assistant_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
assistant_model.to(device)

model_id = "openai/whisper-large-v2"

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,
    generate_kwargs={"assistant_model": assistant_model},
    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"])

Additional Speed & Memory Improvements

You can apply additional speed and memory improvements to Distil-Whisper which we cover in the following.

Flash Attention

We recommend using Flash-Attention 2 if your GPU allows for it. To do so, you first need to install Flash Attention:

pip install flash-attn --no-build-isolation

and then all you have to do is to pass use_flash_attention_2=True to from_pretrained:

- model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
+ model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, use_flash_attention_2=True)

Torch Scale-Product-Attention (SDPA)

If your GPU does not support Flash Attention, we recommend making use of BetterTransformers. To do so, you first need to install optimum:

pip install --upgrade optimum

And then convert your model to a "BetterTransformer" model before using it:

model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
+ model = model.to_bettertransformer()

Running Distil-Whisper in openai-whisper

To use the model in the original Whisper format, first ensure you have the openai-whisper package installed:

pip install --upgrade openai-whisper

The following code-snippet demonstrates how to transcribe a sample file from the LibriSpeech dataset loaded using πŸ€— Datasets:

import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from whisper import load_model, transcribe

distil_large_v2 = hf_hub_download(repo_id="distil-whisper/distil-large-v2", filename="original-model.bin")
model = load_model(distil_large_v2)

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

pred_out = transcribe(model, audio=sample)
print(pred_out["text"])

To transcribe a local audio file, simply pass the path to the audio file as the audio argument to transcribe:

pred_out = transcribe(model, audio="audio.mp3")

Whisper.cpp

Distil-Whisper can be run from the Whisper.cpp repository with the original sequential long-form transcription algorithm. In a provisional benchmark on Mac M1, distil-large-v2 is 2x faster than large-v2, while performing to within 0.1% WER over long-form audio.

Note that future releases of Distil-Whisper will target faster CPU inference more! By distilling smaller encoders, we aim to achieve similar speed-ups to what we obtain on GPU.

Steps for getting started:

  1. Clone the Whisper.cpp repository:
git clone https://github.com/ggerganov/whisper.cpp.git
cd whisper.cpp
  1. Download the ggml weights for distil-medium.en from the Hugging Face Hub:
python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='distil-whisper/distil-large-v2', filename='ggml-large-32-2.en.bin', local_dir='./models')"

Note that if you do not have the huggingface_hub package installed, you can also download the weights with wget:

wget https://huggingface.co/distil-whisper/distil-large-v2/resolve/main/ggml-large-32-2.en.bin -P ./models
  1. Run inference using the provided sample audio:
make -j && ./main -m models/ggml-large-32-2.en.bin -f samples/jfk.wav

Transformers.js

import { pipeline } from '@xenova/transformers';

let transcriber = await pipeline('automatic-speech-recognition', 'distil-whisper/distil-large-v2');

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let output = await transcriber(url);
// { text: " And so, my fellow Americans, ask not what your country can do for you. Ask what you can do for your country." }

See the docs for more information.

Note: Due to the large model size, we recommend running this model server-side with Node.js (instead of in-browser).

Candle

Through an integration with Hugging Face Candle πŸ•―οΈ, Distil-Whisper is now available in the Rust library πŸ¦€

Benefit from:

  • Optimised CPU backend with optional MKL support for x86 and Accelerate for Macs
  • CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL
  • WASM support: run Distil-Whisper in a browser

Steps for getting started:

  1. Install candle-core as explained here
  2. Clone the candle repository locally:
git clone https://github.com/huggingface/candle.git
  1. Enter the example directory for Whisper:
cd candle/candle-examples/examples/whisper
  1. Run an example:
cargo run --example whisper --release -- --model distil-large-v2
  1. To specify your own audio file, add the --input flag:
cargo run --example whisper --release -- --model distil-large-v2 --input audio.wav

8bit & 4bit Quantization

Coming soon ...

Whisper.cpp

Coming soon ...

Model Details

Distil-Whisper inherits the encoder-decoder architecture from Whisper. The encoder maps a sequence of speech vector inputs to a sequence of hidden-state vectors. The decoder auto-regressively predicts text tokens, conditional on all previous tokens and the encoder hidden-states. Consequently, the encoder is only run forward once, whereas the decoder is run as many times as the number of tokens generated. In practice, this means the decoder accounts for over 90% of total inference time. Thus, to optimise for latency, the focus should be on minimising the inference time of the decoder.

To distill the Whisper model, we reduce the number of decoder layers while keeping the encoder fixed. The encoder (shown in green) is entirely copied from the teacher to the student and frozen during training. The student's decoder consists of only two decoder layers, which are initialised from the first and last decoder layer of the teacher (shown in red). All other decoder layers of the teacher are discarded. The model is then trained on a weighted sum of the KL divergence and pseudo-label loss terms.

Evaluation

The following code-snippets demonstrates how to evaluate the Distil-Whisper model on the LibriSpeech validation.clean dataset with streaming mode, meaning no audio data has to be downloaded to your local device.

First, we need to install the required packages, including πŸ€— Datasets to stream and load the audio data, and πŸ€— Evaluate to perform the WER calculation:

pip install --upgrade pip
pip install --upgrade transformers datasets[audio] evaluate jiwer

Evaluation can then be run end-to-end with the following example:

from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
from datasets import load_dataset
from evaluate import load
import torch
from tqdm import tqdm

# define our torch configuration
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-v2"

# load the model + processor
model =  AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, use_safetensors=True, low_cpu_mem_usage=True)
model = model.to(device)
processor = AutoProcessor.from_pretrained(model_id)

# load the dataset with streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)

# define the evaluation metric
wer_metric = load("wer")
normalizer = EnglishTextNormalizer(processor.tokenizer.english_spelling_normalizer)

def inference(batch):
    # 1. Pre-process the audio data to log-mel spectrogram inputs
    audio = [sample["array"] for sample in batch["audio"]]
    input_features = processor(audio, sampling_rate=batch["audio"][0]["sampling_rate"], return_tensors="pt").input_features
    input_features = input_features.to(device, dtype=torch_dtype)
    
    # 2. Auto-regressively generate the predicted token ids
    pred_ids = model.generate(input_features, max_new_tokens=128, language="en", task="transcribe")
    
    # 3. Decode the token ids to the final transcription
    batch["transcription"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
    batch["reference"] = batch["text"]
    return batch

dataset = dataset.map(function=inference, batched=True, batch_size=16)

all_transcriptions = []
all_references = []

# iterate over the dataset and run inference
for i, result in tqdm(enumerate(dataset), desc="Evaluating..."):
    all_transcriptions.append(result["transcription"])
    all_references.append(result["reference"])

# normalize predictions and references
all_transcriptions = [normalizer(transcription) for transcription in all_transcriptions]
all_references = [normalizer(reference) for reference in all_references]

# compute the WER metric
wer = 100 * wer_metric.compute(predictions=all_transcriptions, references=all_references)
print(wer)

Print Output:

2.983685535968466

Intended Use

Distil-Whisper is intended to be a drop-in replacement for Whisper on English speech recognition. In particular, it achieves comparable WER results over out-of-distribution test data, while being 6x faster over both short and long-form audio.

Data

Distil-Whisper is trained on 22,000 hours of audio data from 9 open-source, permissively licensed speech datasets on the Hugging Face Hub:

Dataset Size / h Speakers Domain Licence
People's Speech 12,000 unknown Internet Archive CC-BY-SA-4.0
Common Voice 13 3,000 unknown Narrated Wikipedia CC0-1.0
GigaSpeech 2,500 unknown Audiobook, podcast, YouTube apache-2.0
Fisher 1,960 11,900 Telephone conversations LDC
LibriSpeech 960 2,480 Audiobooks CC-BY-4.0
VoxPopuli 540 1,310 European Parliament CC0
TED-LIUM 450 2,030 TED talks CC-BY-NC-ND 3.0
SwitchBoard 260 540 Telephone conversations LDC
AMI 100 unknown Meetings CC-BY-4.0
Total 21,770 18,260+

The combined dataset spans 10 distinct domains and over 50k speakers. The diversity of this dataset is crucial to ensuring the distilled model is robust to audio distributions and noise.

The audio data is then pseudo-labelled using the Whisper large-v2 model: we use Whisper to generate predictions for all the audio in our training set and use these as the target labels during training. Using pseudo-labels ensures that the transcriptions are consistently formatted across datasets and provides sequence-level distillation signal during training.

WER Filter

The Whisper pseudo-label predictions are subject to mis-transcriptions and hallucinations. To ensure we only train on accurate pseudo-labels, we employ a simple WER heuristic during training. First, we normalise the Whisper pseudo-labels and the ground truth labels provided by each dataset. We then compute the WER between these labels. If the WER exceeds a specified threshold, we discard the training example. Otherwise, we keep it for training.

Section 9.2 of the Distil-Whisper paper demonstrates the effectiveness of this filter for improving downstream performance of the distilled model. We also partially attribute Distil-Whisper's robustness to hallucinations to this filter.

Training

The model was trained for 80,000 optimisation steps (or eight epochs). The Tensorboard training logs can be found under: https://huggingface.co/distil-whisper/distil-large-v2/tensorboard?params=scalars#frame

Results

The distilled model performs to within 1% WER of Whisper on out-of-distribution (OOD) short-form audio, and outperforms Whisper by 0.1% on OOD long-form audio. This performance gain is attributed to lower hallucinations.

For a detailed per-dataset breakdown of the evaluation results, refer to Tables 16 and 17 of the Distil-Whisper paper

Distil-Whisper is also evaluated on the ESB benchmark datasets as part of the OpenASR leaderboard, where it performs to within 0.2% WER of Whisper.

Reproducing Distil-Whisper

Training and evaluation code to reproduce Distil-Whisper is available under the Distil-Whisper repository: https://github.com/huggingface/distil-whisper/tree/main/training

License

Distil-Whisper inherits the MIT license from OpenAI's Whisper model.

Citation

If you use this model, please consider citing the Distil-Whisper paper:

@misc{gandhi2023distilwhisper,
      title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling}, 
      author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
      year={2023},
      eprint={2311.00430},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Acknowledgements

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