Piano Transcription GGUF

Architecture

Input: 16 kHz mono audio
  -> STFT (n_fft=2048, hop=160) -> LogMel (229 bins, 30-8000 Hz) -> BatchNorm
  -> 4x AcousticModelCRnn8Dropout (frame / onset / offset / velocity):
       4x ConvBlock (Conv2d 3x3 + BN2d + ReLU + AvgPool2d(1,2))
       FC(1792->768) + BN1d + ReLU
       2-layer BiGRU (768->256) -> FC(512->88) -> sigmoid
  -> Onset refinement: cat(onset, sqrt(onset)*velocity) -> BiGRU -> FC -> sigmoid
  -> Frame refinement: cat(frame, onset, offset) -> BiGRU -> FC -> sigmoid
  -> Post-processing: regression binarization -> note detection -> MIDI events
Output: note events with onset/offset times, MIDI note (21-108), velocity (0-127)

Files

Usage with CrispASR

Source

• Original: qiuqiangkong/piano_transcription_inference
• Paper: Q. Kong et al., "High-resolution Piano Transcription with Pedals by Regressing Onsets and Offsets Times," 2021
• License: Apache-2.0
• Checkpoint: Zenodo 4034264 (note F1=0.9677, pedal F1=0.9186)

Converter

Provenance and EU AI Act Art. 53 note

• Upstream model: bytedance/piano_transcription_inference.
• Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.