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This commit is contained in:
Shuming Liu
2026-08-18 09:08:21 +08:00
parent 7c81976b2b
commit a36e776343
6 changed files with 368 additions and 42 deletions
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7
ios/OralTrainer.xcodeproj/project.xcworkspace/contents.xcworkspacedata generated Normal file
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@@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<Workspace
version = "1.0">
<FileRef
location = "self:">
</FileRef>
</Workspace>

BIN
ios/OralTrainer.xcodeproj/project.xcworkspace/xcuserdata/liushuming.xcuserdatad/UserInterfaceState.xcuserstate generated Normal file
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14
ios/OralTrainer.xcodeproj/xcuserdata/liushuming.xcuserdatad/xcschemes/xcschememanagement.plist Normal file
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@@ -0,0 +1,14 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>SchemeUserState</key>
<dict>
<key>OralTrainer.xcscheme_^#shared#^_</key>
<dict>
<key>orderHint</key>
<integer>0</integer>
</dict>
</dict>
</dict>
</plist>

47
sentence_api/audio_metrics.py
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@@ -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:

138
sentence_api/tests/test_transcription.py Normal file
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@@ -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)

202
sentence_api/transcription.py
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@@ -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.")
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
duration = _wav_duration_seconds(audio_path)
if duration is None or duration > CHUNK_SECONDS + 2.0:
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
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"
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"\[(?P<speaker>S\d+)\]"