prj-beatrice
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japanese-hubert-base-phoneme-ctc

Automatic Speech Recognition

Transformers

TensorBoard

Safetensors

Japanese

hubert

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+license: apache-2.0
+language: ja
+datasets: reazon-research/reazonspeech
+base_model: rinna/japanese-hubert-base
 ---
+# japanese-hubert-base-phoneme-ctc
+rinna/japanese-hubert-base を CTC での日本語音素認識にファインチューニングしたモデルです。
+## モデル概要
+- ReazonSpeech v2 データセットを使用し、pyopenjtalk-plus で生成した音素ラベルを正解と見做して rinna/japanese-hubert-base をファインチューニング
+- 0.3 エポック程度学習したのち、JSUT コーパス (ラベル: https://github.com/sarulab-speech/jsut-label) に対する精度が最も良いチェックポイントを選択
+## ハイパーパラメータ
+* 学習率
+  * CTC Head: 2e-5
+  * 他: 2e-6
+* バッチサイズ: 32
+* 最大音声サンプル数: 250000
+* 最適化: AdamW
+  * betas: (0.9, 0.98)
+  * weight_decay: 0.01
+* 学習率スケジューリング: Cosine
+  * Warmup ステップ数: 10000
+  * 最大ステップ数: 800000
+    * ただし、途中で JSUT での精度が改善されなくなったため 200000 ステップで打ち切り
+## 使用例
+```python
+import librosa
+import numpy as np
+import torch
+from transformers import HubertForCTC, Wav2Vec2Processor
+MODEL_NAME = "prj-beatrice/japanese-hubert-base-phoneme-ctc"
+model = HubertForCTC.from_pretrained(MODEL_NAME)
+processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
+audio, sr = librosa.load("audio.wav", sr=16000)
+audio = np.concatenate([np.zeros(sr), audio, np.zeros(sr // 2)])
+inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+    predicted_ids = outputs.logits.argmax(-1)
+    phonemes = processor.decode(predicted_ids[0], spaces_between_special_tokens=True)
+print(phonemes)
+# => "m i z u o m a r e e sh i a k a r a k a w a n a k U t e w a n a r a n a i n o d e s U"
+```
+## 学習環境
+- A100 80GB
+- Python 3.10.12
+```
+absl-py==2.3.0
+accelerate==1.7.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.13
+aiosignal==1.3.2
+annotated-types==0.7.0
+async-timeout==5.0.1
+attrs==25.3.0
+audioread==3.0.1
+certifi==2025.6.15
+cffi==1.17.1
+charset-normalizer==3.4.2
+click==8.2.1
+coloredlogs==15.0.1
+coverage==7.9.1
+datasets==3.6.0
+decorator==5.2.1
+dill==0.3.8
+evaluate==0.4.3
+exceptiongroup==1.3.0
+filelock==3.18.0
+flatbuffers==25.2.10
+frozenlist==1.7.0
+fsspec==2025.3.0
+gitdb==4.0.12
+gitpython==3.1.44
+grpcio==1.73.0
+hf-xet==1.1.3
+huggingface-hub==0.33.0
+humanfriendly==10.0
+idna==3.10
+iniconfig==2.1.0
+jinja2==3.1.6
+jiwer==3.1.0
+joblib==1.5.1
+lazy-loader==0.4
+librosa==0.11.0
+llvmlite==0.44.0
+markdown==3.8
+markupsafe==3.0.2
+mpmath==1.3.0
+msgpack==1.1.1
+multidict==6.4.4
+multiprocess==0.70.16
+networkx==3.4.2
+numba==0.61.2
+numpy==2.2.6
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cuda-cupti-cu12==12.6.80
+nvidia-cuda-nvrtc-cu12==12.6.77
+nvidia-cuda-runtime-cu12==12.6.77
+nvidia-cudnn-cu12==9.5.1.17
+nvidia-cufft-cu12==11.3.0.4
+nvidia-cufile-cu12==1.11.1.6
+nvidia-curand-cu12==10.3.7.77
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cusparse-cu12==12.5.4.2
+nvidia-cusparselt-cu12==0.6.3
+nvidia-nccl-cu12==2.26.2
+nvidia-nvjitlink-cu12==12.6.85
+nvidia-nvtx-cu12==12.6.77
+onnxruntime==1.22.0
+packaging==25.0
+pandas==2.3.0
+platformdirs==4.3.8
+pluggy==1.6.0
+pooch==1.8.2
+propcache==0.3.2
+protobuf==6.31.1
+psutil==7.0.0
+pyarrow==20.0.0
+pycparser==2.22
+pydantic==2.11.7
+pydantic-core==2.33.2
+pygments==2.19.1
+pyopenjtalk-plus==0.4.1.post3
+pytest==8.4.0
+pytest-cov==6.2.1
+python-dateutil==2.9.0.post0
+pytz==2025.2
+pyyaml==6.0.2
+rapidfuzz==3.13.0
+regex==2024.11.6
+requests==2.32.4
+ruff==0.11.13
+safetensors==0.5.3
+scikit-learn==1.7.0
+scipy==1.15.3
+sentry-sdk==2.30.0
+setproctitle==1.3.6
+setuptools==80.9.0
+six==1.17.0
+smmap==5.0.2
+soundfile==0.13.1
+soxr==0.5.0.post1
+sudachidict-core==20250515
+sudachipy==0.6.10
+sympy==1.14.0
+tensorboard==2.19.0
+tensorboard-data-server==0.7.2
+threadpoolctl==3.6.0
+tokenizers==0.21.1
+tomli==2.2.1
+torch==2.7.1
+torchaudio==2.7.1
+tqdm==4.67.1
+transformers==4.52.4
+triton==3.3.1
+typing-extensions==4.14.0
+typing-inspection==0.4.1
+tzdata==2025.2
+urllib3==2.4.0
+wandb==0.20.1
+werkzeug==3.1.3
+xxhash==3.5.0
+yarl==1.20.1
+```