torchcrepe
Pytorch implementation of CREPE pitch tracker
Decision gist · record as of 2026-08-14
Yes, if you need robust pitch estimation from audio. The package is well-established (first released 2020-08-01), has low install friction, carries no known vulnerabilities, and remains actively maintained. The aging status (455 days since last release) reflects stable maturity rather than abandonment. MIT licensing poses no restrictions. Primary gotcha: PyTorch must be installed separately and configured for your hardware before use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- PyTorch must be installed first via system-dependent instructions at pytorch.org; GPU acceleration requires CUDA-compatible hardware and appropriate PyTorch build.
- Low friction installation with a pure Python wheel.
- The package is aging (last release 455 days ago) but the repository remains active with recent commits.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions. You may use, modify, and distribute this package freely as long as you include the original license notice.
last release 2025-05-16 (455 days) · last repo commit 2025-05-16 · 523 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 295,755 downloads/mo, #7,915 on PyPI
Alternatives
Verify before relying
pip install torchcrepe
import torchcrepe
audio, sr = torchcrepe.load.audio('file.wav')
pitch = torchcrepe.predict(audio, sr, hop_length=int(sr / 200.), fmin=50, fmax=550, model='tiny', device='cpu')- Whether the package works with recent PyTorch versions (no minimum version specified in metadata)
- Performance characteristics and inference speed on typical hardware
- Accuracy comparison with the original TensorFlow CREPE implementation
What it is and what it does
torchcrepe is a PyTorch port of the CREPE convolutional pitch estimation model, originally developed for robust pitch tracking in speech and music. It loads pre-trained model weights (available in 'tiny' and 'full' variants) converted from the original TensorFlow implementation and uses them to extract pitch and periodicity estimates from audio.
The package provides multiple entry points: direct pitch prediction from audio arrays, batch processing from files, and extraction of intermediate embeddings from the fifth max-pooling layer. It includes post-processing tools for filtering noisy periodicity values, thresholding unreliable pitch estimates, and decoding strategies (Viterbi, weighted argmax, argmax) to reduce octave errors. A command-line interface and file-to-file convenience functions support workflow integration.
Use it for
- Extract pitch contours from speech recordings for prosody analysis or voice conversion applications.
- Analyze melody in music recordings for transcription, similarity matching, or music information retrieval tasks.
- Generate pitch embeddings from audio as pretrained features for downstream voice or music classification models.
- Batch process large audio corpora to compute pitch statistics or detect voiced/unvoiced regions.
- Build real-time pitch tracking pipelines for live audio applications using GPU acceleration.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need robust pitch estimation from audio.
The package is well-established (first released 2020-08-01), has low install friction, carries no known vulnerabilities, and remains actively maintained. The aging status (455 days since last release) reflects stable maturity rather than abandonment. MIT licensing poses no restrictions. Primary gotcha: PyTorch must be installed separately and configured for your hardware before use.
Install
torchcrepe on PyPI
Before you install
Low friction installation with a pure Python wheel. The package is aging (last release 455 days ago) but the repository remains active with recent commits. Requires PyTorch installation first, which is a system-dependent step documented in the package instructions.
PyTorch must be installed first via system-dependent instructions at pytorch.org; GPU acceleration requires CUDA-compatible hardware and appropriate PyTorch build.
License in practice
MIT license permits commercial and private use with minimal restrictions. You may use, modify, and distribute this package freely as long as you include the original license notice.
Quickstart
pip install torchcrepe
import torchcrepe
audio, sr = torchcrepe.load.audio('file.wav')
pitch = torchcrepe.predict(audio, sr, hop_length=int(sr / 200.), fmin=50, fmax=550, model='tiny', device='cpu')Verify before relying
- Whether the package works with recent PyTorch versions (no minimum version specified in metadata)
- Performance characteristics and inference speed on typical hardware
- Accuracy comparison with the original TensorFlow CREPE implementation
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packageslibrosaresampyscipytorchtorchaudiotqdm |
| Maintenance | Aging 455 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 295,755 / month, #7,915 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT License |
Evidence: torchcrepe-0.0.24-py3-none-any.whl
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