kotoba-whisper-v1.0 / README.md

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metadata

license: apache-2.0
language: ja
tags:
  - audio
  - automatic-speech-recognition
  - hf-asr-leaderboard
widget:
  - example_title: CommonVoice 8.0 (Test Split)
    src: >-
      https://huggingface.co/datasets/japanese-asr/ja_asr.common_voice_8_0/resolve/main/sample.flac
  - example_title: JSUT Basic 5000
    src: >-
      https://huggingface.co/datasets/japanese-asr/ja_asr.jsut_basic5000/resolve/main/sample.flac
  - example_title: ReazonSpeech (Test Split)
    src: >-
      https://huggingface.co/datasets/japanese-asr/ja_asr.reazonspeech_test/resolve/main/sample.flac
pipeline_tag: automatic-speech-recognition
metrics:
  - wer
model-index:
  - name: kotoba-tech/kotoba-whisper-v1.0
    results:
      - task:
          type: automatic-speech-recognition
        dataset:
          name: japanese-asr/ja_asr.common_voice_8_0
          type: japanese-asr/ja_asr.common_voice_8_0
        metrics:
          - name: WER
            type: WER
            value: null
      - task:
          type: automatic-speech-recognition
        dataset:
          name: japanese-asr/ja_asr.reazonspeech_test
          type: japanese-asr/ja_asr.reazonspeech_test
        metrics:
          - name: WER
            type: WER
            value: null
      - task:
          type: automatic-speech-recognition
        dataset:
          name: japanese-asr/ja_asr.reazonspeech_test
          type: japanese-asr/ja_asr.reazonspeech_test
        metrics:
          - name: WER
            type: WER
            value: null

Kotoba-Whisper

Kotoba-Whisper is a collection of distilled Whisper models for Japanese ASR. Following the original work of distil-whisper (Robust Knowledge Distillation via Large-Scale Pseudo Labelling), we employ OpenAI's Whisper large-v3 as the teacher model, and the student model that consists the full encoder of the teacher whisper model, and a decoder with two layers initialized from the first and last layer of the whisper model. As the initial version, we release kotoba-whisper-v1.0 trained on the large subset of ReazonSpeech, which amounts 1,253 hours of audio with 16,861,235 characters of transcriptions (5 sec audio with 18 text tokens in average).

Benchmark

kotoba-whisper-v1.0 achieves better WER than the openai/whisper-large-v3 in the in-domain held-out test set from ReazonSpeech, and achieves competitive WER on the out-of-domain test set including JSUT basic 5000 and the Japanese subset from CommonVoice 8.0.

model	CommonVoice 8.0 (Japanese)	JSUT Basic 5000	ReazonSpeech Test
kotoba-tech/kotoba-whisper-v1.0	59.27	64.36	56.62
openai/whisper-large-v3	55.41	59.34	60.23
openai/whisper-medium	63.64	69.52	76.04
openai/whisper-small	74.21	82.02	82.99
openai/whisper-tiny	93.78	97.72	94.85

Latency

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.

Model	Params / M	Rel. Latency
large-v3	1550	1.0
distil-large-v3	756	6.3

The models are shared via huggingfacae, and the distillation code was adapted from official distil-whisper training scripts, which we release in this repository. As the training dataset, we employ ReazonSpeech, one of the largest speech and text paired dataset in Japanese, and we evaluate our ASR models on JSUT basic 5000 and the Japanese subset from CommonVoice 8.0, as well as a held-out test set from ReazonSpeech.

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.

Transformers Usage
Library Integrations
- Whisper cpp
- Faster Whisper
Model Details

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 = "kotoba-tech/kotoba-whisper-v1.0"

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"])

For more control over the generation parameters, use the model + processor API directly:

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 = "kotoba-tech/kotoba-whisper-v1.0"

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"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
).input_features

input_features = input_features.to(device, dtype=torch_dtype)

gen_kwargs = {
  "max_new_tokens": 128,
  "num_beams": 1,
  "return_timestamps": False,
}

pred_ids = model.generate(input_features, **gen_kwargs)
pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=gen_kwargs["return_timestamps"])

print(pred_text)

Sequential Long-Form

Unlike previous Distil-Whisper releases, distil-large-v3 is specifically designed to be compatible with OpenAI's sequential long-form transcription algorithm. This algorithm uses a sliding window for buffered inference of long audio files (> 30-seconds), and returns more accurate transcriptions compared to the chunked long-form algorithm.

The sequential long-form algorithm should be used in either of the following scenarios:

Transcription accuracy is the most important factor, and latency is less of a consideration
You are transcribing batches of long audio files, in which case the latency of sequential is comparable to chunked, while being up to 0.5% WER more accurate

If you are transcribing single long audio files and latency is the most important factor, you should use the chunked algorithm described below. For a detailed explanation of the different algorithms, refer to Sections 5 of the Distil-Whisper paper.

The pipeline class can be used to transcribe long audio files with the sequential algorithm 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 = "kotoba-tech/kotoba-whisper-v1.0"

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

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

For more control over the generation parameters, use the model + processor API directly:

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 = "kotoba-tech/kotoba-whisper-v1.0"

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"]

inputs = processor(
    sample["array"],
    sampling_rate=sample["sampling_rate"],
    return_tensors="pt",
    truncation=False,
    padding="longest",
    return_attention_mask=True,
)
inputs = inputs.to(device, dtype=torch_dtype)

gen_kwargs = {
    "max_new_tokens": 448,
    "num_beams": 1,
    "condition_on_prev_tokens": False,
    "compression_ratio_threshold": 1.35,  # zlib compression ratio threshold (in token space)
    "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
    "logprob_threshold": -1.0,
    "no_speech_threshold": 0.6,
    "return_timestamps": True,
}

pred_ids = model.generate(**i   nputs, **gen_kwargs)
pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False)

print(pred_text)

Chunked Long-Form

distil-large-v3 remains compatible with the Transformers chunked long-form algorithm. This algorithm should be used when a single large audio file is being transcribed and the fastest possible inference is required. In such circumstances, the chunked algorithm is up to 9x faster than OpenAI's sequential long-form implementation (see Table 7 of the Distil-Whisper paper).

To enable chunking, pass the chunk_length_s parameter to the pipeline. For distil-large-v3, a chunk length of 25-seconds is optimal. To activate batching over long audio files, 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 = "kotoba-tech/kotoba-whisper-v1.0"

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=25,
    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"])

Additional Speed & Memory Improvements

You can apply additional speed and memory improvements to Distil-Whisper to further reduce the inference speed and VRAM requirements. These optimisations primarily target the attention kernel, swapping it from an eager implementation to a more efficient flash attention version.

Flash Attention 2

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

Then pass attn_implementation="flash_attention_2" 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, attn_implementation="flash_attention_2")

Torch Scale-Product-Attention (SDPA)

If your GPU does not support Flash Attention, we recommend making use of PyTorch scaled dot-product attention (SDPA). This attention implementation is activated by default for PyTorch versions 2.1.1 or greater. To check whether you have a compatible PyTorch version, run the following Python code snippet:

from transformers.utils import is_torch_sdpa_available

print(is_torch_sdpa_available())

If the above returns True, you have a valid version of PyTorch installed and SDPA is activated by default. If it returns False, you need to upgrade your PyTorch version according to the official instructions

Once a valid PyTorch version is installed, SDPA is activated by default. It can also be set explicitly by specifying attn_implementation="sdpa" as follows:

- 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, attn_implementation="sdpa")

Library Integrations

Whisper.cpp

Distil-Whisper can be run with the Whisper.cpp package with the original sequential long-form transcription algorithm. In a provisional benchmark on Mac M1, distil-large-v3 is over 5x faster than Whisper large-v3, while performing to within 0.8% WER over long-form audio.

Steps for getting started:

Clone the Whisper.cpp repository:

git clone https://github.com/ggerganov/whisper.cpp.git
cd whisper.cpp

Install the Hugging Face Hub Python package:

pip install --upgrade huggingface_hub

And download the GGML weights for distil-large-v3 using the following Python snippet:

from huggingface_hub import hf_hub_download

hf_hub_download(repo_id='kotoba-tech/kotoba-whisper-v1.0-ggml', filename='ggml-distil-large-v3.bin', local_dir='./models')

Note that if you do not have a Python environment set-up, you can also download the weights directly with wget:

wget https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-ggml/resolve/main/ggml-distil-large-v3.bin -P ./models

Run inference using the provided sample audio:

make -j && ./main -m models/ggml-distil-large-v3.bin -f samples/jfk.wav

Faster-Whisper

Faster-Whisper is a reimplementation of Whisper using CTranslate2, a fast inference engine for Transformer models.

First, install the Faster-Whisper package according to the official instructions. 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 git+https://github.com/SYSTRAN/faster-whisper datasets[audio]

The following code snippet loads the distil-large-v3 model and runs inference on an example file from the LibriSpeech ASR dataset:

import torch
from faster_whisper import WhisperModel
from datasets import load_dataset

# define our torch configuration
device = "cuda:0" if torch.cuda.is_available() else "cpu"
compute_type = "float16" if torch.cuda.is_available() else "float32"

# load model on GPU if available, else cpu
model = WhisperModel("distil-large-v3", device=device, compute_type=compute_type)

# load toy dataset for example
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[1]["audio"]["path"]

segments, info = model.transcribe(sample, beam_size=1)

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

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

segments, info = model.transcribe("audio.mp3", beam_size=1)

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 is 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 a subset of the teacher decoder layers, which are intialised from maximally spaced layers. 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 🤗 Datasets to stream and load the audio data, and 🤗 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 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 = "kotoba-tech/kotoba-whisper-v1.0"

# 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")

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)
    
    # 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

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

all_transcriptions = []
all_references = []

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

# normalize predictions and references
all_transcriptions = [processor.normalize(transcription) for transcription in all_transcriptions]
all_references = [processor.normalize(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:

2.428920763531516

Data

Distil-Whisper is trained on 22,000 hours of audio data from nine 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-v3 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 11 epochs) with batch size 256. The Tensorboard training logs can be found under: https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0/tensorboard?params=scalars#frame

Results

The distilled model performs to within 1.5% WER of Whisper large-v3 on out-of-distribution (OOD) short-form audio, within 1% WER on sequential long-form decoding, and outperforms large-v3 by 0.1% on chunked long-form. 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 Kotoba-Whisper

Training and evaluation code to reproduce Kotoba-Whisper is available at the repository: TBA.

Acknowledgements

OpenAI for the Whisper model.
Hugging Face 🤗 Transformers for the model integration.
Hugging Face 🤗 for sharing the Distil-Whisper codebase.

kotoba-tech
/

kotoba-whisper-v1.0