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<h1>Hugging Face for Audio: Resources ✨</h1>
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<b>Audio transformers course</b>: https://huggingface.co/learn/audio-course/chapter0/introduction#course-structure. This covers the standard tasks (ASR, TTS, audio classification) with notes on using pre-trained models and fine-tuning. See also Unit 7 for a speaker diarization application.
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<h2>Using pre-trained models</h2>
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<li>With pipelines: https://www.reddit.com/r/MachineLearning/comments/16xshji/d_the_most_complete_audio_ml_toolkit/</li>
<li>Transformers docs: https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer</li>
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<h2>Training</h2>
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<li>Datasets https://huggingface.co/blog/audio-datasets</li>
<li>Fine-tune Whisper for ASR https://huggingface.co/blog/fine-tune-whisper</li>
<li>Distil Whisper for ASR https://github.com/huggingface/distil-whisper/tree/main/training</li>
<li>Fine-tune VITS for TTS https://twitter.com/yoachlacombe/status/1735348885369889264</li>
<li>Fine-tune Wav2Vec2 for audio class https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification</li>
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<h2>Optimisation</h2>
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<li>Whisper JAX for ASR https://github.com/sanchit-gandhi/whisper-jax</li>
<li>Distil Whisper for ASR https://github.com/huggingface/distil-whisper/tree/main</li>
<li>Insanely Fast Whisper for ASR https://github.com/Vaibhavs10/insanely-fast-whisper</li>
<li>Speculative decoding with Whisper for ASR https://huggingface.co/blog/whisper-speculative-decoding</li>
<li>Bark for TTS https://huggingface.co/blog/optimizing-bark</li>
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<h2>Deployment</h2>
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<li>Endpoint https://huggingface.co/blog/run-musicgen-as-an-api</li>
<li>Gradio client https://www.gradio.app/docs/client (e.g. for Whisper https://huggingface.co/spaces/hf-audio/whisper-large-v3)</li>
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