| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - audio-classification |
| - speech |
| - music |
| - pytorch |
| pipeline_tag: audio-classification |
| --- |
| |
| # SMAD Audio Classifier |
|
|
| Classifies audio into four labels: |
|
|
| | id | label | meaning | |
| |---:|---|---| |
| | 0 | `speech_noise` | spoken voice over non-music noise | |
| | 1 | `speech_music` | spoken voice over a music bed | |
| | 2 | `singing_music` | sung voice over music | |
| | 3 | `none` | no human voice: instrumental music, noise, or silence | |
|
|
| ## Install |
|
|
| ```bash |
| pip install torch librosa soundfile numpy |
| ``` |
|
|
| ## Use |
|
|
| ```python |
| from predict_crnn import SmadClassifier |
| |
| clf = SmadClassifier() |
| result = clf.predict("audio.mp3") |
| |
| print(result["overall"]) |
| print(result["share"]) |
| ``` |
|
|
| CLI: |
|
|
| ```bash |
| python predict_crnn.py audio.mp3 --json |
| ``` |
|
|
| Each 4-second window includes a label, confidence, and per-class probabilities. |
| The `overall` label is the class covering the most windows in the file. |
|
|
