| """SearchAudio — fully-local semantic search over long Hindi audio talks. | |
| Type an English sentence (or speak/upload a Hindi clip) and jump straight to the | |
| moment in a recording where that topic is discussed. Everything runs offline on a | |
| single Windows PC with an NVIDIA GPU. | |
| Pipeline: audio -> ffmpeg normalize -> WhisperX ASR (word timestamps) -> | |
| sentence-packed chunks -> bge-m3 embeddings -> LanceDB index. | |
| Query: text/Hindi-audio -> bge-m3 -> LanceDB hybrid search -> bge-reranker -> results. | |
| """ | |
| __version__ = "0.1.0" | |
| # --------------------------------------------------------------------------- | |
| # Silence a spurious torchcodec warning from pyannote.audio. | |
| # | |
| # WhisperX imports ``whisperx.diarize`` (via its VAD modules), which does | |
| # ``from pyannote.audio import Pipeline`` at module top. pyannote.audio, in turn, | |
| # tries ``import torchcodec`` and — if the native libs can't load — prints a large | |
| # multi-line UserWarning ("torchcodec is not installed correctly so built-in audio | |
| # decoding will fail ..."). | |
| # | |
| # On this Windows box torchcodec's DLLs can't load (the FFmpeg *shared* libraries | |
| # aren't on the DLL search path, and the system FFmpeg is v8, which torchcodec 0.7 | |
| # doesn't support). That is harmless for us: SearchAudio never lets torchcodec/pyannote | |
| # decode anything. We normalize every input to 16 kHz mono WAV with the bundled | |
| # ffmpeg.exe, read it with the stdlib ``wave`` module into a numpy array, and hand that | |
| # array to WhisperX + Silero VAD (see app/asr.py). No diarization is used. That is | |
| # exactly the "provide audio in-memory" path the warning itself recommends, so the | |
| # message is pure noise. Filter just this one warning; leave all others intact. | |
| import warnings as _warnings | |
| _warnings.filterwarnings( | |
| "ignore", | |
| message=r"(?s).*torchcodec is not installed correctly.*", | |
| category=UserWarning, | |
| ) | |