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README.md

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 ---
-library_name: transformers
-tags: []
+language: ja
+license: apache-2.0
+tags:
+- audio
+- automatic-speech-recognition
+- hf-asr-leaderboard
+metrics:
+- wer
+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
+model-index:
+- name: kotoba-tech/kotoba-whisper-v2.1
+  results:
+  - task:
+      type: automatic-speech-recognition
+    dataset:
+      name: CommonVoice_8.0 (Japanese)
+      type: japanese-asr/ja_asr.common_voice_8_0
+    metrics:
+    - type: WER
+      value: 59.27
+      name: WER
+    - type: CER
+      value: 9.44
+      name: CER
+  - task:
+      type: automatic-speech-recognition
+    dataset:
+      name: ReazonSpeech (Test)
+      type: japanese-asr/ja_asr.reazonspeech_test
+    metrics:
+    - type: WER
+      value: 56.62
+      name: WER
+    - type: CER
+      value: 12.6
+      name: CER
+  - task:
+      type: automatic-speech-recognition
+    dataset:
+      name: JSUT Basic5000
+      type: japanese-asr/ja_asr.jsut_basic5000
+    metrics:
+    - type: WER
+      value: 64.36
+      name: WER
+    - type: CER
+      value: 8.48
+      name: CER
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-
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-## Model Details
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-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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-## Training Details
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+# Kotoba-Whisper-v2.1
+_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 
+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)
+
+
+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))
+along with the.
+
+
+| model                                                    |   CommonVoice 8.0 (Japanese) |   JSUT Basic 5000 |  ReazonSpeech Test |
+|:---------------------------------------------------------|---------------------------------------:|-------------------------------------:|----------------------------------------:|
+| kotoba-tech/kotoba-whisper-v2.0                          |                                   15.6 |                                 15.2 |                                    17.8 |
+| kotoba-tech/kotoba-whisper-v2.1 (punctuator + stable-ts) |                                   13.7 |                                 ***11.2*** |                                    ***17.4*** |
+| kotoba-tech/kotoba-whisper-v2.1 (punctuator)             |                                   13.9 |                                 11.4 |                                    18   |
+| kotoba-tech/kotoba-whisper-v2.1 (stable-ts)              |                                   15.7 |                                 15   |                                    17.7 |
+| kotoba-tech/kotoba-whisper-v1.0                          |                                   15.6 |                                 15.2 |                                    17.8 |
+| kotoba-tech/kotoba-whisper-v1.1 (punctuator + stable-ts) |                                   13.7 |                                 ***11.2*** |                                    ***17.4*** |
+| kotoba-tech/kotoba-whisper-v1.1 (punctuator)             |                                   13.9 |                                 11.4 |                                    18   |
+| kotoba-tech/kotoba-whisper-v1.1 (stable-ts)              |                                   15.7 |                                 15   |                                    17.7 |
+| openai/whisper-large-v3                                  |                                   ***12.9*** |                                 13.4 |                                    20.6 |
+
+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).
+
+### Latency
+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).
+
+## Transformers Usage
+Kotoba-Whisper-v2.1 is supported in the Hugging Face 🤗 Transformers library from version 4.39 onwards. To run the model, first 
+install the latest version of Transformers.
+
+```bash
+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`](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": "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,
+    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,
+```
+
+### Transcription with Prompt
+Kotoba-whisper can generate transcription with prompting as below:
+
+```python
+import re
+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": "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](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).