fff

sentence_api/audio_metrics.py

@@ -57,12 +57,38 @@ def analyze_audio(path: Path) -> AudioMetrics:
 
 
 def analyze_samples(samples: np.ndarray, sample_rate: int = SAMPLE_RATE) -> AudioMetrics:
+    if samples.ndim != 1:
+        samples = samples.reshape(-1)
+    speech = speech_frame_mask(samples, sample_rate)
+    speech_indexes = np.flatnonzero(speech)
+    if speech_indexes.size == 0:
+        raise AudioAnalysisError("No usable speech was detected in the recording.")
+
+    recording_duration_ms = max(1, int(round(samples.size / sample_rate * 1000)))
+    window = max(1, int(round(sample_rate * 0.03)))
+    frame_ms = window / sample_rate * 1000
+    speech_duration_ms = max(1, int(round(speech.sum() * frame_ms)))
+    first = int(speech_indexes[0])
+    last = int(speech_indexes[-1])
+    internal_frames = max(1, last - first + 1)
+    internal_silence_frames = int((~speech[first : last + 1]).sum())
+    internal_silence_ms = int(round(internal_silence_frames * frame_ms))
+    internal_pause_ratio = internal_silence_frames / internal_frames
+    return AudioMetrics(
+        recording_duration_ms=recording_duration_ms,
+        speech_duration_ms=speech_duration_ms,
+        internal_silence_ms=internal_silence_ms,
+        internal_pause_ratio=round(internal_pause_ratio, 4),
+    )
+
+
+def speech_frame_mask(samples: np.ndarray, sample_rate: int = SAMPLE_RATE) -> np.ndarray:
+    """Per-frame speech mask (30 ms frames) using the same RMS threshold as analyze_samples."""
     if samples.ndim != 1:
         samples = samples.reshape(-1)
     if samples.size == 0 or sample_rate <= 0:
         raise AudioAnalysisError("The uploaded audio is empty.")
 
-    recording_duration_ms = max(1, int(round(samples.size / sample_rate * 1000)))
     window = max(1, int(round(sample_rate * 0.03)))
     complete_frames = int(np.ceil(samples.size / window))
     padded = np.pad(samples, (0, complete_frames * window - samples.size))
@@ -79,24 +105,7 @@ def analyze_samples(samples: np.ndarray, sample_rate: int = SAMPLE_RATE) -> Audi
     # Treat very short gaps inside a word as speech, then reject short clicks.
     _bridge_false_runs(speech, max_frames=5)
     _remove_true_runs(speech, max_frames=2)
-    speech_indexes = np.flatnonzero(speech)
-    if speech_indexes.size == 0:
-        raise AudioAnalysisError("No usable speech was detected in the recording.")
-
-    frame_ms = window / sample_rate * 1000
-    speech_duration_ms = max(1, int(round(speech.sum() * frame_ms)))
-    first = int(speech_indexes[0])
-    last = int(speech_indexes[-1])
-    internal_frames = max(1, last - first + 1)
-    internal_silence_frames = int((~speech[first : last + 1]).sum())
-    internal_silence_ms = int(round(internal_silence_frames * frame_ms))
-    internal_pause_ratio = internal_silence_frames / internal_frames
-    return AudioMetrics(
-        recording_duration_ms=recording_duration_ms,
-        speech_duration_ms=speech_duration_ms,
-        internal_silence_ms=internal_silence_ms,
-        internal_pause_ratio=round(internal_pause_ratio, 4),
-    )
+    return speech
 
 
 def _bridge_false_runs(values: np.ndarray, max_frames: int) -> None:

sentence_api/tests/test_transcription.py

@@ -0,0 +1,138 @@
+import io
+import wave
+
+import numpy as np
+import pytest
+
+from sentence_api.transcription import (
+    MossTranscriber,
+    _parse_json_segments,
+    _plan_chunks,
+    _wav_bytes,
+    _wav_duration_seconds,
+)
+
+
+SAMPLE_RATE = 16000
+
+
+def _speech(seconds: float, sample_rate: int = SAMPLE_RATE) -> np.ndarray:
+    t = np.arange(int(seconds * sample_rate)) / sample_rate
+    return 0.25 * np.sin(2 * np.pi * 220 * t)
+
+
+def _silence(seconds: float, sample_rate: int = SAMPLE_RATE) -> np.ndarray:
+    return np.zeros(int(seconds * sample_rate))
+
+
+def _audio(*parts: np.ndarray, sample_rate: int = SAMPLE_RATE) -> np.ndarray:
+    return np.concatenate([np.asarray(part) for part in parts]).astype(np.float32)
+
+
+def test_plan_chunks_cuts_at_silence_gaps():
+    samples = _audio(
+        _speech(10),
+        _silence(0.8),
+        _speech(29.2),
+        _silence(1.0),
+        _speech(29.0),
+        _silence(0.6),
+        _speech(24.4),
+    )
+    chunks = _plan_chunks(samples, SAMPLE_RATE)
+    assert chunks[0][0] == pytest.approx(0.0)
+    assert chunks[-1][1] == pytest.approx(95.0)
+    for (start, end), (next_start, _) in zip(chunks, chunks[1:]):
+        assert end == pytest.approx(next_start)
+        assert end - start <= 34.0
+    assert 10.0 <= chunks[0][1] <= 10.8
+    assert 40.0 <= chunks[1][1] <= 41.0
+    assert 70.0 <= chunks[2][1] <= 70.6
+
+
+def test_plan_chunks_falls_back_to_fixed_windows():
+    chunks = _plan_chunks(_speech(95), SAMPLE_RATE)
+    assert chunks == [(0.0, 30.0), (30.0, 60.0), (60.0, 90.0), (90.0, 95.0)]
+
+
+def test_plan_chunks_merges_short_tail():
+    chunks = _plan_chunks(_speech(93), SAMPLE_RATE)
+    assert chunks == [(0.0, 30.0), (30.0, 60.0), (60.0, 93.0)]
+
+
+def test_parse_json_segments_filters_hallucinated_segments():
+    raw = [
+        {"start": 0.0, "end": 3.0, "text": "hello world", "compression_ratio": 1.4},
+        {"start": 3.5, "end": 6.0, "text": "hello world", "compression_ratio": 12.0},
+        {"start": 6.5, "end": 9.0, "text": "no ratio field"},
+    ]
+    filtered = _parse_json_segments(raw, compression_limit=2.4)
+    assert [segment.text for segment in filtered] == ["hello world", "no ratio field"]
+    assert len(_parse_json_segments(raw)) == 3
+
+
+def test_wav_bytes_roundtrip(tmp_path):
+    samples = _speech(2.0)
+    data = _wav_bytes(samples, SAMPLE_RATE)
+    assert data.startswith(b"RIFF")
+    path = tmp_path / "chunk.wav"
+    path.write_bytes(data)
+    assert _wav_duration_seconds(path) == pytest.approx(2.0, abs=0.05)
+    with wave.open(str(path), "rb") as wav:
+        assert wav.getnchannels() == 1
+        assert wav.getsampwidth() == 2
+        assert wav.getframerate() == SAMPLE_RATE
+
+
+def test_wav_duration_returns_none_for_non_wav(tmp_path):
+    path = tmp_path / "note.txt"
+    path.write_text("not audio")
+    assert _wav_duration_seconds(path) is None
+
+
+def test_chunked_transcribe_offsets_timestamps(monkeypatch):
+    requests = []
+    call_count = {"n": 0}
+
+    def fake_post(endpoint, data=None, files=None, timeout=None):
+        call_count["n"] += 1
+        requests.append(data)
+        audio = files["file"][1].read()
+        with wave.open(io.BytesIO(audio), "rb") as wav:
+            duration = wav.getnframes() / wav.getframerate()
+        if call_count["n"] == 1:
+            segments = [
+                {"start": 1.0, "end": 4.0, "text": "first part", "compression_ratio": 1.4},
+                {"start": 27.0, "end": 30.0, "text": "second part", "compression_ratio": 1.5},
+            ]
+        elif call_count["n"] == 2:
+            segments = [
+                {"start": 1.0, "end": 4.0, "text": "third part", "compression_ratio": 1.4},
+                {"start": 27.0, "end": 30.0, "text": "fourth part", "compression_ratio": 1.5},
+            ]
+        else:
+            segments = [{"start": 0.5, "end": 3.5, "text": "tail part", "compression_ratio": 1.3}]
+        assert abs(duration - 30.0) < 1.0 or abs(duration - 5.0) < 1.0
+
+        class Response:
+            def raise_for_status(self):
+                pass
+
+            def json(self):
+                return {"text": "", "segments": segments}
+
+        return Response()
+
+    monkeypatch.setattr("sentence_api.transcription.httpx.post", fake_post)
+    transcriber = MossTranscriber(endpoint="http://whisper:9000", model="whisper")
+    result = transcriber._transcribe_chunked(_speech(65), SAMPLE_RATE, None)
+    assert [(s.start_seconds, s.end_seconds, s.text) for s in result.segments] == [
+        (1.0, 4.0, "first part"),
+        (27.0, 30.0, "second part"),
+        (31.0, 34.0, "third part"),
+        (57.0, 60.0, "fourth part"),
+        (60.5, 63.5, "tail part"),
+    ]
+    assert result.text == "first part second part third part fourth part tail part"
+    assert len(requests) == 3
+    assert all(request.get("condition_on_previous_text") == "false" for request in requests)

sentence_api/transcription.py

@@ -1,10 +1,28 @@
+import io
 import mimetypes
 import re
+import wave
 from dataclasses import dataclass
 from pathlib import Path
 from typing import List, Optional, Protocol
 
 import httpx
+import numpy as np
+
+from .audio_metrics import AudioAnalysisError, decode_audio_mono, speech_frame_mask
+
+
+# Whisper was trained on 30-second clips. Sending longer audio in a single request
+# makes the model enter a repetition loop (every later segment becomes the same
+# sentence while timestamps keep advancing), so long audio is split into short
+# chunks at silence gaps and each chunk is transcribed independently.
+CHUNK_SECONDS = 30.0
+MIN_CHUNK_SECONDS = 5.0
+MIN_SILENCE_SECONDS = 0.5
+MIN_TAIL_SECONDS = 4.0
+# Whisper's own repetition-loop detector: segments that compress this well are
+# almost certainly hallucinated repeated text (healthy speech is ~1.3-1.8).
+COMPRESSION_RATIO_LIMIT = 2.4
 
 
 @dataclass(frozen=True)
@@ -50,29 +68,25 @@ class MossTranscriber:
     def transcribe(self, audio_path: Path, language: Optional[str] = None) -> Transcript:
         if not self.available:
             raise RuntimeError("MOSS transcription is not configured on this server.")
+        duration = _wav_duration_seconds(audio_path)
+        if duration is None or duration > CHUNK_SECONDS + 2.0:
+            try:
+                samples, sample_rate = decode_audio_mono(audio_path)
+            except AudioAnalysisError:
+                return self._transcribe_single(audio_path, language)
+            if samples.size / sample_rate > CHUNK_SECONDS + 2.0:
+                try:
+                    return self._transcribe_chunked(samples, sample_rate, language)
+                except AudioAnalysisError:
+                    return self._transcribe_single(audio_path, language)
+        return self._transcribe_single(audio_path, language)
+
+    def _transcribe_single(
+        self, audio_path: Path, language: Optional[str]
+    ) -> Transcript:
         content_type = mimetypes.guess_type(audio_path.name)[0] or "application/octet-stream"
-        data = {
-            "model": self.model,
-            "response_format": "verbose_json",
-            "temperature": "0",
-            "max_new_tokens": str(self.max_new_tokens),
-        }
-        if language:
-            data["language"] = language
-
-        try:
-            with audio_path.open("rb") as audio_file:
-                response = httpx.post(
-                    self.endpoint,
-                    data=data,
-                    files={"file": (audio_path.name, audio_file, content_type)},
-                    timeout=httpx.Timeout(self.timeout_seconds, connect=30),
-                )
-            response.raise_for_status()
-            payload = response.json()
-        except (httpx.HTTPError, ValueError) as exc:
-            raise RuntimeError(f"MOSS transcription request failed: {exc}") from exc
-
+        with audio_path.open("rb") as audio_file:
+            payload = self._post_audio(audio_file, audio_path.name, content_type, language)
         raw_text = str(payload.get("text") or "").strip()
         segments = _parse_json_segments(payload.get("segments"))
         if not segments:
@@ -80,8 +94,71 @@ class MossTranscriber:
         plain_text = " ".join(segment.text for segment in segments).strip() or raw_text
         return Transcript(text=plain_text, segments=segments)
 
+    def _transcribe_chunked(
+        self, samples: np.ndarray, sample_rate: int, language: Optional[str]
+    ) -> Transcript:
+        segments: List[TranscriptionSegment] = []
+        for start_seconds, end_seconds in _plan_chunks(samples, sample_rate):
+            chunk = samples[
+                int(round(start_seconds * sample_rate)) : int(round(end_seconds * sample_rate))
+            ]
+            payload = self._post_audio(
+                io.BytesIO(_wav_bytes(chunk, sample_rate)),
+                "chunk.wav",
+                "audio/wav",
+                language,
+            )
+            chunk_segments = _parse_json_segments(
+                payload.get("segments"), compression_limit=COMPRESSION_RATIO_LIMIT
+            )
+            if not chunk_segments:
+                chunk_segments = _parse_compact_segments(str(payload.get("text") or "").strip())
+            for segment in chunk_segments:
+                segments.append(
+                    TranscriptionSegment(
+                        start_seconds=segment.start_seconds + start_seconds,
+                        end_seconds=segment.end_seconds + start_seconds,
+                        text=segment.text,
+                        speaker=segment.speaker,
+                    )
+                )
+        segments.sort(key=lambda item: (item.start_seconds, item.end_seconds))
+        plain_text = " ".join(segment.text for segment in segments).strip()
+        return Transcript(text=plain_text, segments=segments)
 
-def _parse_json_segments(raw_segments) -> List[TranscriptionSegment]:
+    def _post_audio(
+        self,
+        audio_file,
+        filename: str,
+        content_type: str,
+        language: Optional[str],
+    ) -> dict:
+        data = {
+            "model": self.model,
+            "response_format": "verbose_json",
+            "temperature": "0",
+            "condition_on_previous_text": "false",
+            "max_new_tokens": str(self.max_new_tokens),
+        }
+        if language:
+            data["language"] = language
+
+        try:
+            response = httpx.post(
+                self.endpoint,
+                data=data,
+                files={"file": (filename, audio_file, content_type)},
+                timeout=httpx.Timeout(self.timeout_seconds, connect=30),
+            )
+            response.raise_for_status()
+            return response.json()
+        except (httpx.HTTPError, ValueError) as exc:
+            raise RuntimeError(f"MOSS transcription request failed: {exc}") from exc
+
+
+def _parse_json_segments(
+    raw_segments, compression_limit: Optional[float] = None
+) -> List[TranscriptionSegment]:
     if not isinstance(raw_segments, list):
         return []
     segments = []
@@ -93,6 +170,13 @@ def _parse_json_segments(raw_segments) -> List[TranscriptionSegment]:
             end = float(item["end"])
         except (KeyError, TypeError, ValueError):
             continue
+        compression_ratio = item.get("compression_ratio")
+        if (
+            compression_limit is not None
+            and isinstance(compression_ratio, (int, float))
+            and compression_ratio > compression_limit
+        ):
+            continue
         text = str(item.get("text") or "").strip()
         if text and end > start >= 0:
             speaker = item.get("speaker")
@@ -107,6 +191,80 @@ def _parse_json_segments(raw_segments) -> List[TranscriptionSegment]:
     return segments
 
 
+def _wav_duration_seconds(path: Path) -> Optional[float]:
+    try:
+        with wave.open(str(path), "rb") as wav:
+            if wav.getframerate() > 0:
+                return wav.getnframes() / wav.getframerate()
+    except (wave.Error, OSError):
+        pass
+    return None
+
+
+def _wav_bytes(samples: np.ndarray, sample_rate: int) -> bytes:
+    pcm = np.clip(np.round(samples * 32767), -32768, 32767).astype("<i2")
+    buffer = io.BytesIO()
+    with wave.open(buffer, "wb") as wav:
+        wav.setnchannels(1)
+        wav.setsampwidth(2)
+        wav.setframerate(sample_rate)
+        wav.writeframes(pcm.tobytes())
+    return buffer.getvalue()
+
+
+def _plan_chunks(samples: np.ndarray, sample_rate: int) -> List[tuple]:
+    speech = speech_frame_mask(samples, sample_rate)
+    frame_seconds = 0.03
+    gap_frames = max(1, int(round(MIN_SILENCE_SECONDS / frame_seconds)))
+
+    gaps: List[tuple] = []
+    run_start: Optional[int] = None
+    for index, is_speech in enumerate(speech):
+        if not is_speech and run_start is None:
+            run_start = index
+        elif is_speech and run_start is not None:
+            if index - run_start >= gap_frames:
+                gaps.append((run_start * frame_seconds, index * frame_seconds))
+            run_start = None
+    if run_start is not None and len(speech) - run_start >= gap_frames:
+        gaps.append((run_start * frame_seconds, len(speech) * frame_seconds))
+
+    duration = samples.size / sample_rate
+    cuts: List[float] = []
+    cursor = 0.0
+    while duration - cursor > CHUNK_SECONDS + 1e-6:
+        target = cursor + CHUNK_SECONDS
+        best_gap = None
+        for gap_start, gap_end in gaps:
+            if gap_start < cursor + MIN_CHUNK_SECONDS:
+                continue
+            if gap_start > target:
+                break
+            best_gap = (gap_start, gap_end)
+        if best_gap is None:
+            cut = min(target, duration)
+        else:
+            gap_start, gap_end = best_gap
+            cut = min(target, (gap_start + gap_end) / 2.0)
+        cuts.append(cut)
+        cursor = cut
+
+    boundaries = [0.0] + cuts + [duration]
+    chunks = [
+        (boundaries[index], boundaries[index + 1])
+        for index in range(len(boundaries) - 1)
+        if boundaries[index + 1] > boundaries[index] + 0.05
+    ]
+    if (
+        len(chunks) > 1
+        and chunks[-1][1] - chunks[-1][0] < MIN_TAIL_SECONDS
+        and chunks[-1][1] - chunks[-2][0] <= CHUNK_SECONDS + MIN_TAIL_SECONDS
+    ):
+        chunks[-2] = (chunks[-2][0], chunks[-1][1])
+        chunks.pop()
+    return chunks
+
+
 _COMPACT_SEGMENT = re.compile(
     r"\[(?P<start>\d+(?:\.\d+)?)\]"
     r"\[(?P<speaker>S\d+)\]"