Palash Debnath
b4cfd7aac0
Review follow-up. The shared loader resamples soundfile-readable audio to 16 kHz with a linear interpolation, which can alias 44.1/48 kHz input into Whisper's band. PyTorch Whisper now reads such audio at its native rate, as before, so the pipeline's band-limited resampler converts it. Only files soundfile can't open (MP4/M4A) take the ffmpeg path, which resamples properly. New tests: a 48 kHz stereo WAV reaches the pipeline at 48 kHz without ffmpeg, a POST /transcribe with an .m4a returns 200, and the MCP tool names M4A bytes .m4a.
91 lines
3.0 KiB
Python
91 lines
3.0 KiB
Python
"""#2039 — PyTorch Whisper read uploads with soundfile, which cannot open
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MP4/M4A (AAC). /transcribe and the MCP tool both accept .m4a, so every such
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upload failed with "Format not recognised" before ASR ran."""
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import sys
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import numpy as np
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import pytest
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M4A_BYTES = b"\x00\x00\x00\x20ftypM4A " + b"\x00" * 64 # soundfile refuses this
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def _recording_pipe(seen):
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def fake_pipe(inputs, **_kwargs):
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seen.update(inputs)
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return {"text": "hello", "chunks": []}
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return fake_pipe
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def test_an_m4a_upload_reaches_the_pipeline_through_the_ffmpeg_fallback(tmp_path, monkeypatch):
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from services import asr_backend as ab
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clip = tmp_path / "memo.m4a"
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clip.write_bytes(M4A_BYTES)
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decoded = np.zeros(16000, dtype=np.float32)
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decoded_paths = []
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def fake_ffmpeg_decode(path):
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decoded_paths.append(path)
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return decoded
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monkeypatch.setattr(ab, "_decode_audio_16k_mono", fake_ffmpeg_decode)
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seen = {}
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result = ab.PyTorchWhisperBackend(asr_pipe=_recording_pipe(seen)).transcribe(str(clip))
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assert result["text"] == "hello"
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assert decoded_paths == [str(clip)]
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assert seen["sampling_rate"] == 16000
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assert np.array_equal(seen["array"], decoded)
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def test_readable_audio_keeps_its_native_rate(tmp_path, monkeypatch):
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"""No linear resample for WAV/FLAC: the pipeline's own resampler is
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band-limited, a plain interpolation from 48 kHz would alias."""
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import soundfile as sf
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from services import asr_backend as ab
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clip = tmp_path / "take.wav"
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stereo = np.zeros((4800, 2), dtype=np.float32)
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sf.write(str(clip), stereo, 48000)
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def must_not_decode(_path):
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raise AssertionError("soundfile-readable audio must not go through ffmpeg")
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monkeypatch.setattr(ab, "_decode_audio_16k_mono", must_not_decode)
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seen = {}
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ab.PyTorchWhisperBackend(asr_pipe=_recording_pipe(seen)).transcribe(str(clip))
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assert seen["sampling_rate"] == 48000
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assert seen["array"].ndim == 1 and len(seen["array"]) == 4800
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@pytest.mark.usefixtures("asr_model_installed")
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def test_the_transcribe_route_accepts_an_m4a_upload(monkeypatch):
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from fastapi import FastAPI
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from fastapi.testclient import TestClient
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from api.routers import capture
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ab = sys.modules["services.asr_backend"] # the module the route imports from
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monkeypatch.setattr(ab, "asr_model_missing_error", lambda **_kw: None)
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monkeypatch.setattr(ab, "_decode_audio_16k_mono", lambda _path: np.zeros(16000, dtype=np.float32))
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seen = {}
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backend = ab.PyTorchWhisperBackend(asr_pipe=_recording_pipe(seen))
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monkeypatch.setattr(ab, "get_capture_asr_backend", lambda *a, **k: backend)
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app = FastAPI()
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app.include_router(capture.router)
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r = TestClient(app).post(
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"/transcribe", files={"audio": ("memo.m4a", M4A_BYTES, "audio/mp4")}
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)
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assert r.status_code == 200, r.text
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assert seen["sampling_rate"] == 16000
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def test_the_mcp_tool_names_m4a_bytes_as_m4a():
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import mcp_server
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assert mcp_server._sniff_audio_ext(M4A_BYTES) == ".m4a"
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