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* fix(audio): preprocess reference samples instead of rejecting them
Uploaded/recorded voice samples were rejected outright whenever the peak
exceeded 0.99 ("Audio is clipping (reduce input gain)"). That wasn't
actionable: a recording in the app has no pre-gain control, and an
already-captured file can't be re-taken by the user. The Settings
"Normalize audio" toggle only affects generated TTS output, so users who
enabled it expecting it to help with sample uploads were still blocked.
Replace the hard reject with a small, always-on preprocess step that
runs right after load:
- DC-offset removal
- Conservative edge-silence trim (top_db=30) with 100 ms padding kept
- Peak cap at 0.95 if the input peak exceeds that
Duration and RMS checks now run on the preprocessed waveform, so
samples that were previously rejected for being "hot" are accepted and
stored with safe headroom. True in-waveform clipping artifacts still
can't be repaired — peak scaling only prevents downstream re-clipping
during multi-sample combination and TTS inference.
Adds a unit-test file (previously none existed for audio.py).
Fixes #456.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
* fix(audio): raise trim threshold, cap pad at net-neutral
Review feedback on the preprocessor:
1. ``trim_top_db=30`` was labelled "conservative" in the docstring but is
actually *more* aggressive than librosa's default of 60. Normal
speech dynamic range sits around 30 dB, so 30 dB would eat quiet
trailing syllables and soft consonants. Raise the default to 40 dB —
below normal speech dynamic range but still catching obvious edge
silence — and fix the docstring.
2. Unconditional 100 ms edge padding ran even when ``librosa.effects.trim``
removed nothing. For a well-recorded 29.9 s upload that path would
push the waveform past the 30 s ceiling and trigger a spurious "too
long" rejection. Only pad when trimming actually shortened the
audio, and cap the pad so the output never exceeds the input length.
Adds a regression test for the net-neutral length behaviour.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <[email protected]>
Backend Tests
Manual test scripts for debugging and validating backend functionality.
Test Files
test_generation_progress.py
Tests TTS generation with SSE progress monitoring to identify UX issues where users see download progress even when the model is already cached.
Usage:
cd backend
python tests/test_generation_progress.py
Prerequisites:
- Server must be running (
python main.py) - At least one voice profile must exist
test_real_download.py
Tests real model download with SSE progress monitoring.
Usage:
cd backend
# Delete cache first to force fresh download
rm -rf ~/.cache/huggingface/hub/models--openai--whisper-base
python tests/test_real_download.py
Prerequisites:
- Server must be running (
python main.py)
test_progress.py
Unit tests for ProgressManager and HFProgressTracker functionality.
Usage:
cd backend
python tests/test_progress.py
test_check_progress_state.py
Debugging script to inspect the internal state of ProgressManager and TaskManager.
Usage:
cd backend
python tests/test_check_progress_state.py
Notes
These are manual test scripts, not automated unit tests. They're designed for:
- Debugging progress tracking issues
- Validating SSE event streams
- Monitoring real-time download behavior
- Inspecting internal state during development