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: CommonVoice_8.0 (Japanese)
          type: japanese-asr/ja_asr.common_voice_8_0
        metrics:
          - name: WER
            type: WER
            value: TBA
          - name: CER
            type: CER
            value: TBA
      - task:
          type: automatic-speech-recognition
        dataset:
          name: ReazonSpeech (Test)
          type: japanese-asr/ja_asr.reazonspeech_test
        metrics:
          - name: WER
            type: WER
            value: TBA
          - name: CER
            type: CER
            value: TBA
      - task:
          type: automatic-speech-recognition
        dataset:
          name: JSUT Basic5000
          type: japanese-asr/ja_asr.jsut_basic5000
        metrics:
          - name: WER
            type: WER
            value: TBA
          - name: CER
            type: CER
            value: TBA

Kotoba-Whisper-v1.1

Kotoba-Whisper-v1.1 is a Japanese ASR model based on kotoba-tech/kotoba-whisper-v1.0, with additional postprocessing stacks integrated as pipeline. The new features includes (i) improved timestamp achieved by stable-ts and (ii) adding punctuation with punctuators. These libraries are merged into Kotoba-Whisper-v1.1 via pipeline and will be applied seamlessly to the predicted transcription from kotoba-tech/kotoba-whisper-v1.0. The pipeline has been developed through the collaboration between Asahi Ushio and Kotoba Technologies

Transformers Usage

Kotoba-Whisper-v1.1 is supported in the Hugging Face 🤗 Transformers library from version 4.39 onwards. To run the model, first install the latest version of Transformers.

pip install --upgrade pip
pip install --upgrade transformers accelerate torchaudio
pip install stable-ts==2.16.0
pip install punctuators==0.0.5

Transcription

The model can be used with the pipeline class to transcribe audio files as follows:

import torch
from transformers import pipeline
from datasets import load_dataset

# config
model_id = "kotoba-tech/kotoba-whisper-v1.1"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_kwargs = {"attn_implementation": "sdpa"} if torch.cuda.is_available() else {}
generate_kwargs = {"language": "japanese", "task": "transcribe"}

# load model
pipe = pipeline(
    model=model_id,
    torch_dtype=torch_dtype,
    device=device,
    model_kwargs=model_kwargs,
    chunk_length_s=15,
    batch_size=16,
    trust_remote_code=True
)

# load sample audio
dataset = load_dataset("japanese-asr/ja_asr.reazonspeech_test", split="test")
sample = dataset[0]["audio"]

# run inference
result = pipe(sample, return_timestamps=True, generate_kwargs=generate_kwargs)
print(result)

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

- result = pipe(sample, return_timestamps=True, generate_kwargs=generate_kwargs)
+ result = pipe("audio.mp3", return_timestamps=True, generate_kwargs=generate_kwargs)

Transcription with Prompt

Kotoba-whisper can generate transcription with prompting as below:

import re
import torch
from transformers import pipeline
from datasets import load_dataset

# config
model_id = "kotoba-tech/kotoba-whisper-v1.1"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_kwargs = {"attn_implementation": "sdpa"} if torch.cuda.is_available() else {}
generate_kwargs = {"language": "japanese", "task": "transcribe"}

# load model
pipe = pipeline(
    model=model_id,
    torch_dtype=torch_dtype,
    device=device,
    model_kwargs=model_kwargs,
    chunk_length_s=15,
    batch_size=16,
    trust_remote_code=True
)

# load sample audio
dataset = load_dataset("japanese-asr/ja_asr.reazonspeech_test", split="test")

# --- Without prompt ---
text = pipe(dataset[10]["audio"], generate_kwargs=generate_kwargs)['text']
print(text)
# 81歳、力強い走りに変わってきます。

# --- With prompt ---: Let's change `81` to `91`.
prompt = "91歳"
generate_kwargs['prompt_ids'] = pipe.tokenizer.get_prompt_ids(prompt, return_tensors="pt").to(device)
text = pipe(dataset[10]["audio"], generate_kwargs=generate_kwargs)['text']
# currently the pipeline for ASR appends the prompt at the beginning of the transcription, so remove it
text = re.sub(rf"\A\s*{prompt}\s*", "", text)
print(text)
# あっぶったでもスルガさん、91歳、力強い走りに変わってきます。

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_kwargs = {"attn_implementation": "sdpa"} if torch.cuda.is_available() else {}
+ model_kwargs = {"attn_implementation": "flash_attention_2"} if torch.cuda.is_available() else {}

Acknowledgements

OpenAI for the Whisper model.
Hugging Face 🤗 Transformers for the model integration.
Hugging Face 🤗 for the Distil-Whisper codebase.
Reazon Human Interaction Lab for the ReazonSpeech dataset.