初始化项目，由ModelHub XC社区提供模型
Model: TalTechNLP/whisper-large-v3-et-subs Source: Original Platform
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82
README.md
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README.md
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---
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license: mit
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language: et
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tags:
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- audio
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- automatic-speech-recognition
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#widget:
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#- example_title: Librispeech sample 1
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# src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
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#- example_title: Librispeech sample 2
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# src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
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pipeline_tag: automatic-speech-recognition
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base_model:
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- openai/whisper-large-v3
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library_name: transformers
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---
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## Introduction
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This model is OpenAI Whisper large-v3, finetuned on ~770 hours of manually created subtitles from Estonian TV (ETV).
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Therefore, this model does not always create verbatim (word-by-word) subtitles but often rephrases the sentences and
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compresses text, especially in the case of spontaneous speech, hestitations, repetitions, etc. However, the length
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of the generated text chunks almost always conforms to the ETV subtitle requirements (48 characters per line).
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## Usage
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It's a finetuned vesion of Whisper large-v3-turbo and can be therefore used via Hugging Face 🤗 Transformers. To run the model, first install the Transformers
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library. For this example, we'll also install 🤗 Accelerate to reduce the model loading time:
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```bash
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pip install --upgrade pip
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pip install --upgrade transformers accelerate
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```
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The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
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class to transcribe audios of arbitrary length:
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```python
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import torch
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
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from datasets import load_dataset
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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model_id = "TalTechNLP/whisper-large-v3-et-subs"
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
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)
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model.to(device)
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processor = AutoProcessor.from_pretrained(model_id)
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pipe = pipeline(
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"automatic-speech-recognition",
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model=model,
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tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor,
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torch_dtype=torch_dtype,
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device=device,
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)
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audio = "sample.mp3"
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result = pipe(sample, generate_kwargs={"task": "transcribe", "language": "et"})
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print(result)
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```
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## Citation
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```
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@inproceedings{fedorchenko-2025-optimizing,
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title = "Optimizing Estonian {TV} Subtitles with Semi-supervised Learning and {LLMs}",
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author = {Fedorchenko, Artem and Alum{\"a}e, Tanel},
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booktitle = "Proceedings of the 25th Nordic Conference on Computational Linguistics (NoDaLiDa)",
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year = "2025"
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}
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```
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preprocessor_config.json
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"<|bg|>",
|
||||
"<|lt|>",
|
||||
"<|la|>",
|
||||
"<|mi|>",
|
||||
"<|ml|>",
|
||||
"<|cy|>",
|
||||
"<|sk|>",
|
||||
"<|te|>",
|
||||
"<|fa|>",
|
||||
"<|lv|>",
|
||||
"<|bn|>",
|
||||
"<|sr|>",
|
||||
"<|az|>",
|
||||
"<|sl|>",
|
||||
"<|kn|>",
|
||||
"<|et|>",
|
||||
"<|mk|>",
|
||||
"<|br|>",
|
||||
"<|eu|>",
|
||||
"<|is|>",
|
||||
"<|hy|>",
|
||||
"<|ne|>",
|
||||
"<|mn|>",
|
||||
"<|bs|>",
|
||||
"<|kk|>",
|
||||
"<|sq|>",
|
||||
"<|sw|>",
|
||||
"<|gl|>",
|
||||
"<|mr|>",
|
||||
"<|pa|>",
|
||||
"<|si|>",
|
||||
"<|km|>",
|
||||
"<|sn|>",
|
||||
"<|yo|>",
|
||||
"<|so|>",
|
||||
"<|af|>",
|
||||
"<|oc|>",
|
||||
"<|ka|>",
|
||||
"<|be|>",
|
||||
"<|tg|>",
|
||||
"<|sd|>",
|
||||
"<|gu|>",
|
||||
"<|am|>",
|
||||
"<|yi|>",
|
||||
"<|lo|>",
|
||||
"<|uz|>",
|
||||
"<|fo|>",
|
||||
"<|ht|>",
|
||||
"<|ps|>",
|
||||
"<|tk|>",
|
||||
"<|nn|>",
|
||||
"<|mt|>",
|
||||
"<|sa|>",
|
||||
"<|lb|>",
|
||||
"<|my|>",
|
||||
"<|bo|>",
|
||||
"<|tl|>",
|
||||
"<|mg|>",
|
||||
"<|as|>",
|
||||
"<|tt|>",
|
||||
"<|haw|>",
|
||||
"<|ln|>",
|
||||
"<|ha|>",
|
||||
"<|ba|>",
|
||||
"<|jw|>",
|
||||
"<|su|>",
|
||||
"<|yue|>",
|
||||
"<|translate|>",
|
||||
"<|transcribe|>",
|
||||
"<|startoflm|>",
|
||||
"<|startofprev|>",
|
||||
"<|nospeech|>",
|
||||
"<|notimestamps|>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
114852
tokenizer.json
Normal file
114852
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
12996
tokenizer_config.json
Normal file
12996
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
50259
vocab.json
Normal file
50259
vocab.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user