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--- |
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language: ja |
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library_name: transformers |
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license: apache-2.0 |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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widget: |
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- example_title: CommonVoice 8.0 (Test Split) |
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src: >- |
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https://huggingface.co/datasets/japanese-asr/ja_asr.common_voice_8_0/resolve/main/sample.flac |
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- example_title: JSUT Basic 5000 |
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src: >- |
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https://huggingface.co/datasets/japanese-asr/ja_asr.jsut_basic5000/resolve/main/sample.flac |
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- example_title: ReazonSpeech (Test Split) |
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src: >- |
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https://huggingface.co/datasets/japanese-asr/ja_asr.reazonspeech_test/resolve/main/sample.flac |
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pipeline_tag: automatic-speech-recognition |
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datasets: |
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- japanese-asr/whisper_transcriptions.reazonspeech.all |
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- japanese-asr/whisper_transcriptions.reazonspeech.all.wer_10.0 |
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- japanese-asr/whisper_transcriptions.reazonspeech.all.wer_10.0.vectorized |
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--- |
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# Kotoba-Whisper-v2.1 |
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_Kotoba-Whisper-v2.1_ is a Japanese ASR model based on [kotoba-tech/kotoba-whisper-v2.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0), with |
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additional postprocessing stacks integrated as [`pipeline`](https://huggingface.co/docs/transformers/en/main_classes/pipelines). The new features includes |
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adding punctuation with [punctuators](https://github.com/1-800-BAD-CODE/punctuators/tree/main). |
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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). |
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The pipeline has been developed through the collaboration between [Asahi Ushio](https://asahiushio.com) and [Kotoba Technologies](https://twitter.com/kotoba_tech) |
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Following table presents the raw CER (unlike usual CER where the punctuations are removed before computing the metrics, see the evaluation script [here](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.1/blob/main/run_short_form_eval.py)) |
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along with the. |
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| model | [CommonVoice 8 (Japanese test set)](https://huggingface.co/datasets/japanese-asr/ja_asr.common_voice_8_0) | [JSUT Basic 5000](https://huggingface.co/datasets/japanese-asr/ja_asr.jsut_basic5000) | [ReazonSpeech (held out test set)](https://huggingface.co/datasets/japanese-asr/ja_asr.reazonspeech_test) | |
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|:--------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------:|----------------------------------------------------------------------------------------:|------------------------------------------------------------------------------------------------------------:| |
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| [kotoba-tech/kotoba-whisper-v2.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0) | 17.6 | 15.4 | 17.4 | |
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| [kotoba-tech/kotoba-whisper-v2.1](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.1) | 17.7 | 15.4 | 17 | --> |
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| [kotoba-tech/kotoba-whisper-v1.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0) | 17.8 | 15.2 | 17.8 | |
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| [kotoba-tech/kotoba-whisper-v1.1](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.1) | 17.9 | 15 | 17.8 | |
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| [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) | 15.3 | 13.4 | 20.5 | |
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| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 15.9 | 10.6 | 34.6 | |
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| [openai/whisper-large](https://huggingface.co/openai/whisper-large) | 16.6 | 11.3 | 40.7 | |
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| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 17.9 | 13.1 | 39.3 | |
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| [openai/whisper-base](https://huggingface.co/openai/whisper-base) | 34.5 | 26.4 | 76 | |
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| [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 21.5 | 18.9 | 48.1 | |
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| [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 58.8 | 38.3 | 153.3 | |
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Regarding to the normalized CER, since those update from v2.1 will be removed by the normalization, kotoba-tech/kotoba-whisper-v2.1 marks the same CER values as [kotoba-tech/kotoba-whisper-v2.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0). |
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### Latency |
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Please refer to the section of the latency in the kotoba-whisper-v1.1 [here](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.1#latency). |
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## Transformers Usage |
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Kotoba-Whisper-v2.1 is supported in the Hugging Face 🤗 Transformers library from version 4.39 onwards. To run the model, first |
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install the latest version of Transformers. |
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```bash |
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pip install --upgrade pip |
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pip install --upgrade transformers accelerate torchaudio |
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pip install stable-ts==2.16.0 |
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pip install punctuators==0.0.5 |
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``` |
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### Transcription |
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The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) |
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class to transcribe audio files as follows: |
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```python |
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import torch |
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from transformers import pipeline |
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from datasets import load_dataset |
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# config |
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model_id = "kotoba-tech/kotoba-whisper-v2.1" |
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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model_kwargs = {"attn_implementation": "sdpa"} if torch.cuda.is_available() else {} |
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generate_kwargs = {"language": "ja", "task": "transcribe"} |
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# load model |
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pipe = pipeline( |
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model=model_id, |
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torch_dtype=torch_dtype, |
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device=device, |
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model_kwargs=model_kwargs, |
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batch_size=16, |
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trust_remote_code=True, |
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punctuator=True |
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) |
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# load sample audio |
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dataset = load_dataset("japanese-asr/ja_asr.reazonspeech_test", split="test") |
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sample = dataset[0]["audio"] |
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# run inference |
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result = pipe(sample, chunk_length_s=15, return_timestamps=True, generate_kwargs=generate_kwargs) |
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print(result) |
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``` |
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- To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline: |
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```diff |
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- result = pipe(sample, return_timestamps=True, generate_kwargs=generate_kwargs) |
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+ result = pipe("audio.mp3", return_timestamps=True, generate_kwargs=generate_kwargs) |
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``` |
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- To deactivate punctuator: |
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```diff |
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- punctuator=True, |
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+ punctuator=False, |
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``` |
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### Flash Attention 2 |
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We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2) |
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if your GPU allows for it. To do so, you first need to install [Flash Attention](https://github.com/Dao-AILab/flash-attention): |
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``` |
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pip install flash-attn --no-build-isolation |
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``` |
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Then pass `attn_implementation="flash_attention_2"` to `from_pretrained`: |
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```diff |
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- model_kwargs = {"attn_implementation": "sdpa"} if torch.cuda.is_available() else {} |
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+ model_kwargs = {"attn_implementation": "flash_attention_2"} if torch.cuda.is_available() else {} |
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``` |
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## Acknowledgements |
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* [OpenAI](https://openai.com/) for the Whisper [model](https://huggingface.co/openai/whisper-large-v3). |
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* Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) for the model integration. |
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* Hugging Face 🤗 for the [Distil-Whisper codebase](https://github.com/huggingface/distil-whisper). |
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* [Reazon Human Interaction Lab](https://research.reazon.jp/) for the [ReazonSpeech dataset](https://huggingface.co/datasets/reazon-research/reazonspeech). |
