Merge pull request #2042 from debpalash/fix/transcribe-m4a

fix(asr): PyTorch Whisper transcribes M4A through the ffmpeg fallback

CHANGELOG.md

@@ -26,6 +26,7 @@ the frozen-backend fallback mirror it for their toolchains.
 - Stopping a process on macOS no longer fails with "Operation not permitted" when it was already exiting (#2032)
 - A YouTube link blocked by its "not a bot" check now says how to attach signed-in cookies in Dub, instead of quoting yt-dlp's command-line flags (#2036, #2034)
 - An engine that fails to start now says whether it timed out, crashed (with its exit code and last output) or answered wrongly, instead of "did not signal ready: None" (#2037, #2026)
+- Transcribing an M4A file with PyTorch Whisper works, instead of failing with "Format not recognised" (#2042, #2039)
 
 ### CI
 

backend/services/asr_backend.py

@@ -1401,7 +1401,15 @@ class PyTorchWhisperBackend(ASRBackend):
         import soundfile as sf
         import torch
         self._ensure_pipe()
+        # #2039: libsndfile cannot open MP4/M4A (AAC), which /transcribe and
+        # the MCP tool both accept. Those decode through the validated ffmpeg
+        # path, which resamples to 16 kHz properly. Anything soundfile can
+        # read keeps its native rate, so the pipeline's band-limited
+        # resampler does the conversion rather than a linear interpolation.
+        try:
             audio_np, sr = sf.read(audio_path, dtype="float32")
+        except Exception:
+            audio_np, sr = _decode_audio_16k_mono(audio_path), 16000
         if audio_np.ndim > 1:
             audio_np = audio_np.mean(axis=1)
         bs = 16 if torch.cuda.is_available() else 2

tests/test_transcribe_m4a_2039.py

@@ -0,0 +1,90 @@
+"""#2039 — PyTorch Whisper read uploads with soundfile, which cannot open
+MP4/M4A (AAC). /transcribe and the MCP tool both accept .m4a, so every such
+upload failed with "Format not recognised" before ASR ran."""
+import sys
+
+import numpy as np
+import pytest
+
+M4A_BYTES = b"\x00\x00\x00\x20ftypM4A " + b"\x00" * 64  # soundfile refuses this
+
+
+def _recording_pipe(seen):
+    def fake_pipe(inputs, **_kwargs):
+        seen.update(inputs)
+        return {"text": "hello", "chunks": []}
+
+    return fake_pipe
+
+
+def test_an_m4a_upload_reaches_the_pipeline_through_the_ffmpeg_fallback(tmp_path, monkeypatch):
+    from services import asr_backend as ab
+
+    clip = tmp_path / "memo.m4a"
+    clip.write_bytes(M4A_BYTES)
+    decoded = np.zeros(16000, dtype=np.float32)
+    decoded_paths = []
+
+    def fake_ffmpeg_decode(path):
+        decoded_paths.append(path)
+        return decoded
+
+    monkeypatch.setattr(ab, "_decode_audio_16k_mono", fake_ffmpeg_decode)
+    seen = {}
+    result = ab.PyTorchWhisperBackend(asr_pipe=_recording_pipe(seen)).transcribe(str(clip))
+
+    assert result["text"] == "hello"
+    assert decoded_paths == [str(clip)]
+    assert seen["sampling_rate"] == 16000
+    assert np.array_equal(seen["array"], decoded)
+
+
+def test_readable_audio_keeps_its_native_rate(tmp_path, monkeypatch):
+    """No linear resample for WAV/FLAC: the pipeline's own resampler is
+    band-limited, a plain interpolation from 48 kHz would alias."""
+    import soundfile as sf
+    from services import asr_backend as ab
+
+    clip = tmp_path / "take.wav"
+    stereo = np.zeros((4800, 2), dtype=np.float32)
+    sf.write(str(clip), stereo, 48000)
+
+    def must_not_decode(_path):
+        raise AssertionError("soundfile-readable audio must not go through ffmpeg")
+
+    monkeypatch.setattr(ab, "_decode_audio_16k_mono", must_not_decode)
+    seen = {}
+    ab.PyTorchWhisperBackend(asr_pipe=_recording_pipe(seen)).transcribe(str(clip))
+
+    assert seen["sampling_rate"] == 48000
+    assert seen["array"].ndim == 1 and len(seen["array"]) == 4800
+
+
+@pytest.mark.usefixtures("asr_model_installed")
+def test_the_transcribe_route_accepts_an_m4a_upload(monkeypatch):
+    from fastapi import FastAPI
+    from fastapi.testclient import TestClient
+
+    from api.routers import capture
+
+    ab = sys.modules["services.asr_backend"]  # the module the route imports from
+    monkeypatch.setattr(ab, "asr_model_missing_error", lambda **_kw: None)
+    monkeypatch.setattr(ab, "_decode_audio_16k_mono", lambda _path: np.zeros(16000, dtype=np.float32))
+    seen = {}
+    backend = ab.PyTorchWhisperBackend(asr_pipe=_recording_pipe(seen))
+    monkeypatch.setattr(ab, "get_capture_asr_backend", lambda *a, **k: backend)
+    app = FastAPI()
+    app.include_router(capture.router)
+
+    r = TestClient(app).post(
+        "/transcribe", files={"audio": ("memo.m4a", M4A_BYTES, "audio/mp4")}
+    )
+
+    assert r.status_code == 200, r.text
+    assert seen["sampling_rate"] == 16000
+
+
+def test_the_mcp_tool_names_m4a_bytes_as_m4a():
+    import mcp_server
+
+    assert mcp_server._sniff_audio_ext(M4A_BYTES) == ".m4a"