293 lines
10 KiB
Python
293 lines
10 KiB
Python
import io
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import mimetypes
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import re
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import wave
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from dataclasses import dataclass
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from pathlib import Path
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from typing import List, Optional, Protocol
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import httpx
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import numpy as np
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from .audio_metrics import AudioAnalysisError, decode_audio_mono, speech_frame_mask
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# Whisper was trained on 30-second clips. Sending longer audio in a single request
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# makes the model enter a repetition loop (every later segment becomes the same
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# sentence while timestamps keep advancing), so long audio is split into short
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# chunks at silence gaps and each chunk is transcribed independently.
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CHUNK_SECONDS = 30.0
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MIN_CHUNK_SECONDS = 5.0
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MIN_SILENCE_SECONDS = 0.5
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MIN_TAIL_SECONDS = 4.0
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# Whisper's own repetition-loop detector: segments that compress this well are
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# almost certainly hallucinated repeated text (healthy speech is ~1.3-1.8).
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COMPRESSION_RATIO_LIMIT = 2.4
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@dataclass(frozen=True)
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class TranscriptionSegment:
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start_seconds: float
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end_seconds: float
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text: str
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speaker: Optional[str] = None
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@dataclass(frozen=True)
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class Transcript:
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text: str
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segments: List[TranscriptionSegment]
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class Transcriber(Protocol):
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@property
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def available(self) -> bool:
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...
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def transcribe(self, audio_path: Path, language: Optional[str] = None) -> Transcript:
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...
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class MossTranscriber:
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def __init__(
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self,
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endpoint: str,
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model: str,
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timeout_seconds: float = 1800,
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max_new_tokens: int = 65536,
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):
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self.endpoint = endpoint.strip()
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self.model = model
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self.timeout_seconds = timeout_seconds
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self.max_new_tokens = max_new_tokens
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@property
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def available(self) -> bool:
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return bool(self.endpoint)
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def transcribe(self, audio_path: Path, language: Optional[str] = None) -> Transcript:
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if not self.available:
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raise RuntimeError("MOSS transcription is not configured on this server.")
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duration = _wav_duration_seconds(audio_path)
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if duration is None or duration > CHUNK_SECONDS + 2.0:
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try:
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samples, sample_rate = decode_audio_mono(audio_path)
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except AudioAnalysisError:
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return self._transcribe_single(audio_path, language)
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if samples.size / sample_rate > CHUNK_SECONDS + 2.0:
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try:
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return self._transcribe_chunked(samples, sample_rate, language)
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except AudioAnalysisError:
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return self._transcribe_single(audio_path, language)
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return self._transcribe_single(audio_path, language)
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def _transcribe_single(
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self, audio_path: Path, language: Optional[str]
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) -> Transcript:
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content_type = mimetypes.guess_type(audio_path.name)[0] or "application/octet-stream"
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with audio_path.open("rb") as audio_file:
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payload = self._post_audio(audio_file, audio_path.name, content_type, language)
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raw_text = str(payload.get("text") or "").strip()
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segments = _parse_json_segments(payload.get("segments"))
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if not segments:
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segments = _parse_compact_segments(raw_text)
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plain_text = " ".join(segment.text for segment in segments).strip() or raw_text
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return Transcript(text=plain_text, segments=segments)
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def _transcribe_chunked(
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self, samples: np.ndarray, sample_rate: int, language: Optional[str]
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) -> Transcript:
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segments: List[TranscriptionSegment] = []
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for start_seconds, end_seconds in _plan_chunks(samples, sample_rate):
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chunk = samples[
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int(round(start_seconds * sample_rate)) : int(round(end_seconds * sample_rate))
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]
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payload = self._post_audio(
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io.BytesIO(_wav_bytes(chunk, sample_rate)),
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"chunk.wav",
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"audio/wav",
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language,
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)
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chunk_segments = _parse_json_segments(
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payload.get("segments"), compression_limit=COMPRESSION_RATIO_LIMIT
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)
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if not chunk_segments:
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chunk_segments = _parse_compact_segments(str(payload.get("text") or "").strip())
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for segment in chunk_segments:
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segments.append(
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TranscriptionSegment(
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start_seconds=segment.start_seconds + start_seconds,
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end_seconds=segment.end_seconds + start_seconds,
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text=segment.text,
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speaker=segment.speaker,
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)
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)
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segments.sort(key=lambda item: (item.start_seconds, item.end_seconds))
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plain_text = " ".join(segment.text for segment in segments).strip()
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return Transcript(text=plain_text, segments=segments)
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def _post_audio(
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self,
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audio_file,
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filename: str,
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content_type: str,
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language: Optional[str],
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) -> dict:
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data = {
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"model": self.model,
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"response_format": "verbose_json",
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"temperature": "0",
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"condition_on_previous_text": "false",
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"max_new_tokens": str(self.max_new_tokens),
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}
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if language:
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data["language"] = language
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try:
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response = httpx.post(
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self.endpoint,
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data=data,
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files={"file": (filename, audio_file, content_type)},
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timeout=httpx.Timeout(self.timeout_seconds, connect=30),
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)
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response.raise_for_status()
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return response.json()
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except (httpx.HTTPError, ValueError) as exc:
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raise RuntimeError(f"MOSS transcription request failed: {exc}") from exc
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def _parse_json_segments(
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raw_segments, compression_limit: Optional[float] = None
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) -> List[TranscriptionSegment]:
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if not isinstance(raw_segments, list):
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return []
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segments = []
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for item in raw_segments:
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if not isinstance(item, dict):
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continue
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try:
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start = float(item["start"])
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end = float(item["end"])
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except (KeyError, TypeError, ValueError):
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continue
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compression_ratio = item.get("compression_ratio")
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if (
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compression_limit is not None
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and isinstance(compression_ratio, (int, float))
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and compression_ratio > compression_limit
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):
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continue
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text = str(item.get("text") or "").strip()
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if text and end > start >= 0:
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speaker = item.get("speaker")
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segments.append(
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TranscriptionSegment(
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start_seconds=start,
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end_seconds=end,
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text=text,
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speaker=str(speaker) if speaker is not None else None,
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)
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)
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return segments
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def _wav_duration_seconds(path: Path) -> Optional[float]:
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try:
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with wave.open(str(path), "rb") as wav:
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if wav.getframerate() > 0:
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return wav.getnframes() / wav.getframerate()
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except (wave.Error, OSError):
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pass
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return None
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def _wav_bytes(samples: np.ndarray, sample_rate: int) -> bytes:
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pcm = np.clip(np.round(samples * 32767), -32768, 32767).astype("<i2")
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buffer = io.BytesIO()
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with wave.open(buffer, "wb") as wav:
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wav.setnchannels(1)
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wav.setsampwidth(2)
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wav.setframerate(sample_rate)
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wav.writeframes(pcm.tobytes())
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return buffer.getvalue()
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def _plan_chunks(samples: np.ndarray, sample_rate: int) -> List[tuple]:
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speech = speech_frame_mask(samples, sample_rate)
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frame_seconds = 0.03
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gap_frames = max(1, int(round(MIN_SILENCE_SECONDS / frame_seconds)))
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gaps: List[tuple] = []
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run_start: Optional[int] = None
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for index, is_speech in enumerate(speech):
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if not is_speech and run_start is None:
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run_start = index
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elif is_speech and run_start is not None:
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if index - run_start >= gap_frames:
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gaps.append((run_start * frame_seconds, index * frame_seconds))
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run_start = None
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if run_start is not None and len(speech) - run_start >= gap_frames:
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gaps.append((run_start * frame_seconds, len(speech) * frame_seconds))
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duration = samples.size / sample_rate
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cuts: List[float] = []
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cursor = 0.0
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while duration - cursor > CHUNK_SECONDS + 1e-6:
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target = cursor + CHUNK_SECONDS
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best_gap = None
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for gap_start, gap_end in gaps:
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if gap_start < cursor + MIN_CHUNK_SECONDS:
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continue
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if gap_start > target:
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break
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best_gap = (gap_start, gap_end)
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if best_gap is None:
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cut = min(target, duration)
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else:
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gap_start, gap_end = best_gap
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cut = min(target, (gap_start + gap_end) / 2.0)
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cuts.append(cut)
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cursor = cut
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boundaries = [0.0] + cuts + [duration]
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chunks = [
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(boundaries[index], boundaries[index + 1])
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for index in range(len(boundaries) - 1)
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if boundaries[index + 1] > boundaries[index] + 0.05
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]
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if (
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len(chunks) > 1
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and chunks[-1][1] - chunks[-1][0] < MIN_TAIL_SECONDS
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and chunks[-1][1] - chunks[-2][0] <= CHUNK_SECONDS + MIN_TAIL_SECONDS
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):
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chunks[-2] = (chunks[-2][0], chunks[-1][1])
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chunks.pop()
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return chunks
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_COMPACT_SEGMENT = re.compile(
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r"\[(?P<start>\d+(?:\.\d+)?)\]"
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r"\[(?P<speaker>S\d+)\]"
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r"(?P<text>.*?)"
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r"\[(?P<end>\d+(?:\.\d+)?)\]",
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flags=re.DOTALL,
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)
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def _parse_compact_segments(text: str) -> List[TranscriptionSegment]:
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segments = []
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for match in _COMPACT_SEGMENT.finditer(text):
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start = float(match.group("start"))
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end = float(match.group("end"))
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segment_text = match.group("text").strip()
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if segment_text and end > start:
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segments.append(
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TranscriptionSegment(
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start_seconds=start,
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end_seconds=end,
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text=segment_text,
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speaker=match.group("speaker"),
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)
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)
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return segments
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