torchaudio's WAV2VEC2ASRBASE960H (wav2vec 2.0 Base fine-tuned on 960 hours of LibriSpeech, from fairseq) converted to Core ML for word-level forced alignment in SwiftHeardLyrics (CTCForcedAligner): it times the words of sung lyrics to a median start error of 38-41 ms on JamendoLyrics, against 86 ms with Apple Speech's timings refined by the vocal envelope. - Input audio: 16 kHz mono, [1, 1600...480000] samples (0.1-30 s). - Output logprobs: [1, frames, 29], one frame per 20 ms, over torchaudio's labels (blank -, word separator |, A-Z and the apostrophe). - float16, except the attention, which computes in float32: in float16 on the CPU it overflows on sung vocals. SwiftHeardLyrics runs it on…
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Models in Library2
Datasets in Library0
Models on Hugging Face4
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Published tasks: Speech recognition.
Reported licenses: apache-2.0, mit. Open a record to review its specific license and conditions.
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Models
Whisper-medium fine-tune for sung lyrics by the HeartMuLa team, converted to WhisperKit's Core ML format for macOS and iOS. SwiftHeardLyrics (HeardLyricsHeart) downloads and runs it, on the GPU or the Neural Engine, and DemucsStudio uses it for lyrics of separated vocals. "Words different" compares with the float32 PyTorch model on 50 MUSDB18-HQ vocal excerpts; the word error rate is against the lyrics of 10 JamendoLyrics songs (2,792 words), with words aligned by a CTC aligner. The 6-bit model needs 1.0 GB of memory on an iPhone's Neural Engine instead of 1.8 GB, runs 1.5x slower on the GPU, and is what SwiftHeardLyrics downloads on iOS. - Converted with whisperkittools (audio encoder…