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Model: reazon-research/japanese-hubert-base-k2-rs35kh
Source: Original Platform
---
 .gitattributes           |   35 +
 README.md                |   91 +
 config.json              |   72 +
 model.safetensors        |    3 +
 preprocessor_config.json |   10 +
 special_tokens_map.json  |    6 +
 tokenizer_config.json    |   41 +
 vocab.json               | 5232 ++++++++++++++++++++++++++++++++++++++
 8 files changed, 5490 insertions(+)
 create mode 100644 .gitattributes
 create mode 100644 README.md
 create mode 100644 config.json
 create mode 100644 model.safetensors
 create mode 100644 preprocessor_config.json
 create mode 100644 special_tokens_map.json
 create mode 100644 tokenizer_config.json
 create mode 100644 vocab.json

diff --git a/.gitattributes b/.gitattributes
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+*.pt filter=lfs diff=lfs merge=lfs -text
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..8862208
--- /dev/null
+++ b/README.md
@@ -0,0 +1,91 @@
+---
+library_name: transformers
+tags:
+- automatic-speech-recognition
+- hubert
+- k2ssl
+datasets:
+- reazon-research/reazonspeech
+language:
+- ja
+metrics:
+- cer
+base_model:
+- reazon-research/japanese-hubert-base-k2
+license: apache-2.0
+pipeline_tag: automatic-speech-recognition
+---
+
+# `japanese-hubert-base-k2-rs35kh`
+
+This model is a [Hubert Base](https://huggingface.co/reazon-research/japanese-hubert-base-k2) fine-tuned on the large-scale Japanese ASR corpus [ReazonSpeech v2.0](https://huggingface.co/datasets/reazon-research/reazonspeech) using the k2 framework.
+
+## Usage
+
+You can use this model through `transformers` library:
+```python
+import librosa
+import numpy as np
+from transformers import AutoProcessor, HubertForCTC
+
+model = HubertForCTC.from_pretrained(
+    "reazon-research/japanese-hubert-base-k2-rs35kh",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+).to("cuda")
+processor = AutoProcessor.from_pretrained("reazon-research/japanese-hubert-base-k2-rs35kh")
+
+audio, _ = librosa.load(audio_filepath, sr=16_000)
+audio = np.pad(audio, pad_width=int(0.5 * 16_000))  # Recommend to pad audio before inference
+input_values = processor(
+    audio,
+    return_tensors="pt",
+    sampling_rate=16_000
+).input_values.to("cuda").to(torch.bfloat16)
+
+with torch.inference_mode():
+    logits = model(input_values).logits.cpu()
+predicted_ids = torch.argmax(logits, dim=-1)[0]
+transcription = processor.decode(predicted_ids, skip_special_tokens=True)
+```
+
+## Test Results
+
+We report the Character Error Rate (CER) of our model and the other wav2vec2 families.
+| Model                                              | #Prameters⬇ |  AVERAGE⬇  | JSUT-BASIC5000⬇ | Common Voice⬇ | TEDxJP-10K⬇ |
+| :------------------------------------------------- | :---------: | :--------: | :-------------: | :-----------: | :---------: |
+| reazon-research/japanese-wav2vec2-large-rs35kh     |     319M    |   16.25%   |     11.00%      |    18.23%     |   19.53%    |
+| reazon-research/japanese-wav2vec2-base-rs35kh      |    96.7M    |   20.40%   |     13.22%      |    23.76%     |   24.23%    |
+| reazon-research/japanese-hubert-base-k2-rs35kh     |    98.4M    |   11.23%   |      9.94%      |    11.59%     |   12.18%    |
+| reazon-research/japanese-hubert-base-k2-rs35kh-bpe |    98.4M    | **11.07%** |    **9.76%**    |  **11.36%**   | **12.10%**  |
+
+We also report the CER for long-form speech.
+| Model                                                   | #Prameters⬇ | JSUT-BOOK⬇ |
+| :------------------------------------------------------ | :---------: | :--------: |
+| reazon-research/japanese-wav2vec2-large-rs35kh          |     319M    |   30.98%   |
+| reazon-research/japanese-wav2vec2-base-rs35kh           |    96.7M    |   82.84%   |
+| reazon-research/japanese-hubert-base-k2-rs35kh          |    98.4M    | **27.05%** |
+|  + [Silero VAD](https://github.com/snakers4/silero-vad) |             | **19.59%** |
+| reazon-research/japanese-hubert-base-k2-rs35kh-bpe      |    98.4M    |   84.55%   |
+|  + [Silero VAD](https://github.com/snakers4/silero-vad) |             | **19.34%** |
+
+## Citation
+```bibtex
+@misc{japanese-hubert-base-k2-rs35kh,
+  title={japanese-hubert-base-k2-rs35kh},
+  author={Sasaki, Yuta},
+  url = {https://huggingface.co/reazon-research/japanese-hubert-base-k2-rs35kh},
+  year = {2025}
+}
+
+@article{yang2024k2ssl,
+  title={k2SSL: A faster and better framework for self-supervised speech representation learning},
+  author={Yang, Yifan and Zhuo, Jianheng and Jin, Zengrui and Ma, Ziyang and Yang, Xiaoyu and Yao, Zengwei and Guo, Liyong and Kang, Wei and Kuang, Fangjun and Lin, Long and others},
+  journal={arXiv preprint arXiv:2411.17100},
+  year={2024}
+}
+```
+
+## License
+
+[Apache Licence 2.0](https://choosealicense.com/licenses/apache-2.0/)
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..c717984
--- /dev/null
+++ b/config.json
@@ -0,0 +1,72 @@
+{
+  "activation_dropout": 0.1,
+  "apply_spec_augment": true,
+  "architectures": [
+    "HubertForCTC"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "conv_bias": false,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_pos_batch_norm": false,
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "do_stable_layer_norm": false,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_norm": "group",
+  "feat_proj_dropout": 0.0,
+  "feat_proj_layer_norm": true,
+  "final_dropout": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "model_type": "hubert",
+  "num_attention_heads": 12,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "transformers_version": "4.56.0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 5230
+}
diff --git a/model.safetensors b/model.safetensors
new file mode 100644
index 0000000..3a395c1
--- /dev/null
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+version https://git-lfs.github.com/spec/v1
+oid sha256:221e373f22a7007ca911d88774382fdf44c82eb609ba12c210086238df176a46
+size 393599760
diff --git a/preprocessor_config.json b/preprocessor_config.json
new file mode 100644
index 0000000..c626b55
--- /dev/null
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+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "Wav2Vec2Processor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000..74fa34b
--- /dev/null
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+  "bos_token": "<sos/eos>",
+  "eos_token": "<sos/eos>",
+  "pad_token": "<blk>",
+  "unk_token": "<unk>"
+}
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000..59a64bd
--- /dev/null
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+  "added_tokens_decoder": {
+    "0": {
+      "content": "<blk>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
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+  },
+  "bos_token": "<sos/eos>",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": false,
+  "eos_token": "<sos/eos>",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<blk>",
+  "processor_class": "Wav2Vec2Processor",
+  "replace_word_delimiter_char": " ",
+  "target_lang": null,
+  "tokenizer_class": "Wav2Vec2CTCTokenizer",
+  "unk_token": "<unk>",
+  "word_delimiter_token": "|"
+}
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