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---
language: ja
library_name: transformers
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
widget:
- example_title: Sample 1
src: https://huggingface.co/kotoba-tech/kotoba-whisper-v2.2/resolve/main/sample_audio/sample_diarization_japanese.mp3
---
# Kotoba-Whisper-v2.2
_Kotoba-Whisper-v2.2_ is a Japanese ASR model based on [kotoba-tech/kotoba-whisper-v2.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0), with
additional postprocessing stacks integrated as [`pipeline`](https://huggingface.co/docs/transformers/en/main_classes/pipelines). The new features includes
(i) improved timestamp achieved by [stable-ts](https://github.com/jianfch/stable-ts) and (ii) adding punctuation with [punctuators](https://github.com/1-800-BAD-CODE/punctuators/tree/main).
These libraries are merged into Kotoba-Whisper-v2.1 via pipeline and will be applied seamlessly to the predicted transcription from [kotoba-tech/kotoba-whisper-v2.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0).
The pipeline has been developed through the collaboration between [Asahi Ushio](https://asahiushio.com) and [Kotoba Technologies](https://twitter.com/kotoba_tech)
## Transformers Usage
Kotoba-Whisper-v2.2 is supported in the Hugging Face 🤗 Transformers library from version 4.39 onwards. To run the model, first
install the latest version of Transformers.
```bash
pip install --upgrade pip
pip install --upgrade transformers accelerate torchaudio
pip install "punctuators==0.0.5"
pip install "pyannote.audio"
pip install git+https://github.com/huggingface/diarizers.git
```
Also,
### Transcription
The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
class to transcribe audio files as follows:
```python
import torch
from transformers import pipeline
from datasets import load_dataset
# config
model_id = "kotoba-tech/kotoba-whisper-v2.1"
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": "ja", "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,
stable_ts=True,
punctuator=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:
```diff
- result = pipe(sample, return_timestamps=True, generate_kwargs=generate_kwargs)
+ result = pipe("audio.mp3", return_timestamps=True, generate_kwargs=generate_kwargs)
```
- To deactivate stable-ts:
```diff
- stable_ts=True,
+ stable_ts=False,
```
- To deactivate punctuator:
```diff
- punctuator=True,
+ punctuator=False,
```
### Flash Attention 2
We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2)
if your GPU allows for it. To do so, you first need to install [Flash Attention](https://github.com/Dao-AILab/flash-attention):
```
pip install flash-attn --no-build-isolation
```
Then pass `attn_implementation="flash_attention_2"` to `from_pretrained`:
```diff
- 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](https://openai.com/) for the Whisper [model](https://huggingface.co/openai/whisper-large-v3).
* Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) for the model integration.
* Hugging Face 🤗 for the [Distil-Whisper codebase](https://github.com/huggingface/distil-whisper).
* [Reazon Human Interaction Lab](https://research.reazon.jp/) for the [ReazonSpeech dataset](https://huggingface.co/datasets/reazon-research/reazonspeech). |