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metadata
library_name: transformers
datasets:
  - reazon-research/reazonspeech
  - joujiboi/japanese-anime-speech
language:
  - ja
  - en
metrics:
  - cer
pipeline_tag: automatic-speech-recognition

Model Card for Model ID

image

Fine tunned ASR model from distil-whisper/distil-large-v2.

This model aimed to transcribe japanese audio especially visual novel.

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Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • Developed by: spow12(yw_nam)
  • Shared by : spow12(yw_nam)
  • Model type: Seq2Seq
  • Language(s) (NLP): japanese
  • Finetuned from model : distil-whisper/distil-large-v2.

Uses

from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
import librosa

processor = AutoProcessor.from_pretrained('spow12/Visual-novel-transcriptor', language="ja", task="transcribe")
model = AutoModelForSpeechSeq2Seq.from_pretrained('spow12/Visual-novel-transcriptor').cuda()
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe")

data, _ = librosa.load(wav_path, sr=16000)
input_features = processor(data, sampling_rate=16000, return_tensors="pt").input_features.cuda()
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription[0])

Bias, Risks, and Limitations

This model trained by japanese dataset included visual novel which contain nsfw content.

Use & Credit

This model is currently available for non-commercial use only. Also, since I'm not detailed in licensing, I hope you use it responsibly.

By sharing this model, I hope to contribute to the research efforts of our community (the open-source community and anime persons).

Citation

@misc {Visual-novel-transcriptor,
    author       = { YoungWoo Nam },
    title        = { Visual-novel-transcriptor },
    year         = 2024,
    url          = { https://huggingface.co/spow12/Visual-novel-transcriptor },
    publisher    = { Hugging Face }
}