83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
---
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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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