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formospeech
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cohere-transcribe-03-2026-taiwanese-hakka

Automatic Speech Recognition
Transformers
Safetensors
Hakka Chinese
Chinese
cohere_asr
audio
speech-recognition
transcription
taiwanese-hakka
hanzi
cohere-transcribe
custom_code
Eval Results (legacy)
Model card Files
xet
 Community
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Model Card for cohere-transcribe-03-2026-taiwanese-hakka

This model is a fine-tuned version of CohereLabs/cohere-transcribe-03-2026 for Taiwanese Hakka automatic speech recognition with Hanzi output.

Training process

The training of the model was performed with the following hyperparameters:

  • Hardware: 4x NVIDIA L40S
  • Per-device batch size: 4
  • Gradient accumulation steps: 32
  • Total training steps: 4795
  • Best checkpoint: step 2877
  • Learning rate: 2e-4
  • Warmup ratio: 0.02
  • Optimizer: adamw_torch_fused
  • LR scheduler type: linear
  • Decoder prompt language: zh
  • Max audio length: 35 seconds

Training data

The model was trained on the following datasets:

  • formospeech/hat_asr_sixian_reading_clean
  • formospeech/hat_asr_sixian_broadcast_clean
  • formospeech/hat_asr_nansixian_reading_clean
  • formospeech/hat_asr_hailu_reading_clean
  • formospeech/hat_tts_hailu_clean
  • formospeech/hat_tts_sixian_clean
  • formospeech/fsr23_eval_clean
  • formospeech/fsr25_warmup_reading_clean
  • formospeech/fsr25_train_clean
  • formospeech/fsr25_final_clean
  • formospeech/fsr25_warmup_media_clean
  • formospeech/hakka_elearning_example_clean
  • formospeech/hakkatv_hanzawa_clean
  • formospeech/hakka_elearning_yt_clean
  • formospeech/hakkaradio_news_clean

Comparison with formospeech/whisper-large-v2-taiwanese-hakka-v1

Model Hailu CER Hailu Norm CER Sixian CER Sixian Norm CER Speed (RTFx)
formospeech/whisper-large-v2-taiwanese-hakka-v1 7.21 3.29 8.69 4.88 144.45
formospeech/cohere-transcribe-03-2026-taiwanese-hakka 10.80 3.99 13.26 5.58 524.88

Speed numbers above are temporary values taken from the Open ASR Leaderboard. They will be replaced later with direct vLLM measurements.

Usage

Access and Authentication

This model is hosted as a gated Hugging Face repository. Before using it:

  1. Visit the model page and request access.
  2. Log in with the same Hugging Face account that has been granted access.
  3. Authenticate your local environment with a Hugging Face access token.

A read token is sufficient for inference.

pip install -U huggingface_hub
hf auth login

Alternatively, you can provide the token through the HF_TOKEN environment variable:

export HF_TOKEN=hf_xxx

Do not hard-code your Hugging Face token in scripts, notebooks, or public repositories.

If you see an error such as Cannot access gated repo, make sure that:

  • your Hugging Face account has been granted access to this model;
  • hf auth whoami shows the expected account;
  • HF_HUB_DISABLE_IMPLICIT_TOKEN is not set.

This model follows the same inference interface as Cohere Transcribe in transformers.

Installation 

pip install "transformers>=5.4.0" torch huggingface_hub soundfile librosa sentencepiece protobuf

Python API 

from transformers import AutoProcessor, CohereAsrForConditionalGeneration
from transformers.audio_utils import load_audio

model_id = "formospeech/cohere-transcribe-03-2026-taiwanese-hakka"

processor = AutoProcessor.from_pretrained(model_id)
model = CohereAsrForConditionalGeneration.from_pretrained(model_id, device_map="auto")

audio = load_audio("path/to/audio.wav", sampling_rate=16000)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt", language="zh")
inputs.to(model.device, dtype=model.dtype)

outputs = model.generate(**inputs, max_new_tokens=256)
text = processor.decode(outputs, skip_special_tokens=True)
print(text)

Notes

  • This release contains inference files only. Optimizer states and trainer checkpoints are intentionally excluded.
  • The tokenizer and processor format follow the upstream Cohere Transcribe release.
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