debpalashandClaude Opus 4.7
994c6cf065
- Add setup router (backend/api/routers/setup.py) for first-run wizard: system checks, engine probes, model downloads with progress - Add translation engines service with pluggable backends - Add utils/hf_progress for HuggingFace download progress streaming - Add PyInstaller runtime hooks (numpy compat, torch compiler disable) - Global exception handler short-circuits h11 LocalProtocolError and Starlette ClientDisconnect with HTTP 499 to silence noisy stack traces when users scrub or cancel video mid-stream - /dub/download-mp3 accepts bitrate query param (clamped 64–320kbps) - Refactor ASR/TTS backends, dub pipeline, engine management - Update backend.spec for PyInstaller packaging - Bump pyproject version to 0.2.0; refresh uv.lock Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
593 lines
24 KiB
Python
593 lines
24 KiB
Python
import os
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import io
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import sys
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import uuid
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import json
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import time
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import asyncio
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import logging
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import shutil
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import subprocess
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import soundfile as sf
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import torch
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import torchaudio
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from typing import Optional, List
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from fastapi import APIRouter, File, Form, UploadFile, HTTPException, Query
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from fastapi.responses import FileResponse, Response, StreamingResponse, JSONResponse
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from core.db import get_db, db_conn
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from core.config import DATA_DIR, DUB_DIR, PREVIEW_DIR, VOICES_DIR
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from core.tasks import task_manager
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from schemas.requests import DubRequest, TranslateRequest, DubIngestUrlRequest
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from services.model_manager import get_model, _gpu_pool, _cpu_pool, get_best_device, get_diarization_pipeline
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from services.audio_dsp import apply_mastering, normalize_audio
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from services.ffmpeg_utils import find_ffmpeg, _get_semaphore, _spawn_with_retry
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from services.segmentation import (
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segment_transcript,
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assign_speakers_from_diarization,
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assign_speakers_heuristic,
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clean_up_segments,
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)
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from services import dub_pipeline
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router = APIRouter()
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logger = logging.getLogger("omnivoice.api")
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# ── Legacy-name aliases to services/dub_pipeline.py ────────────────────────
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# Phase 2.4 moved the business logic into a service. Other routers
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# (dub_generate, dub_translate, dub_export) + internal call sites below still
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# reference the `_get_job` / `_save_job` / `_active_procs` names; those
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# aliases let the transition happen without a repo-wide rename pass.
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#
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# New code should import from `services.dub_pipeline` directly. Aliases can
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# disappear once every caller updates.
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_dub_jobs = dub_pipeline._dub_jobs
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_active_procs = dub_pipeline._active_procs
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_active_procs_lock = dub_pipeline._active_procs_lock
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_DUB_DIR_REAL = dub_pipeline._DUB_DIR_REAL
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_compute_file_hash = dub_pipeline.compute_file_hash
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_find_cached_job = dub_pipeline.find_cached_job
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_safe_job_dir = dub_pipeline.safe_job_dir
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_register_proc = dub_pipeline.register_proc
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_unregister_proc = dub_pipeline.unregister_proc
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_kill_job_procs = dub_pipeline.kill_job_procs
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_get_job = dub_pipeline.get_job
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_save_job = dub_pipeline.save_job
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@router.post("/dub/cleanup-segments/{job_id}")
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def dub_cleanup_segments(job_id: str):
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"""Re-run merge/stitch passes on a job's existing segments to drop fragments."""
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job = _get_job(job_id)
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if not job:
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raise HTTPException(status_code=404, detail="Job not found")
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segments = job.get("segments") or []
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cleaned = clean_up_segments(segments)
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job["segments"] = cleaned
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_save_job(job_id, job)
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return {"segments": cleaned, "before": len(segments), "after": len(cleaned)}
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@router.post("/dub/abort/{job_id}")
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def dub_abort(job_id: str):
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"""Cancel in-flight upload/transcribe subprocesses for a job."""
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with _active_procs_lock:
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had_procs = bool(_active_procs.get(job_id))
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_kill_job_procs(job_id)
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job = _dub_jobs.get(job_id)
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if job is not None:
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job["aborted"] = True
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try:
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task_manager.cancel_task(job_id)
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except Exception:
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pass
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return {"aborted": True, "had_active_procs": had_procs}
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@router.get("/dub/history")
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def list_dub_history():
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conn = get_db()
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try:
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rows = conn.execute("SELECT * FROM dub_history ORDER BY created_at DESC LIMIT 30").fetchall()
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finally:
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conn.close()
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return [dict(r) for r in rows]
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@router.delete("/dub/history")
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def clear_dub_history():
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"""Delete persisted dub rows and their on-disk dirs (scoped to known IDs)."""
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conn = get_db()
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try:
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ids = [r["id"] for r in conn.execute("SELECT id FROM dub_history").fetchall()]
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conn.execute("DELETE FROM dub_history")
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conn.commit()
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finally:
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conn.close()
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for jid in ids:
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safe = _safe_job_dir(jid)
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if safe and os.path.isdir(safe):
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shutil.rmtree(safe, ignore_errors=True)
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return {"cleared": True, "count": len(ids)}
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@router.delete("/dub/history/{history_id}")
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def delete_single_dub_history(history_id: str):
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with db_conn() as conn:
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conn.execute("DELETE FROM dub_history WHERE id=?", (history_id,))
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safe = _safe_job_dir(history_id)
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if safe and os.path.isdir(safe):
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shutil.rmtree(safe, ignore_errors=True)
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_dub_jobs.pop(history_id, None)
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return {"deleted": True}
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@router.post("/preview/upload")
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async def preview_upload(video: UploadFile = File(...)):
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ext = os.path.splitext(video.filename or "video.mp4")[1].lower()
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safe_name = f"{uuid.uuid4().hex[:12]}"
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vid_path = os.path.join(PREVIEW_DIR, f"{safe_name}{ext}")
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wav_path = os.path.join(PREVIEW_DIR, f"{safe_name}.wav")
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with open(vid_path, "wb") as f:
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f.write(await video.read())
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has_audio = False
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if ext not in [".wav", ".mp3", ".m4a", ".aac"]:
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try:
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ffmpeg_cmd = [
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find_ffmpeg(), "-y", "-i", vid_path,
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"-vn", "-acodec", "pcm_s16le", "-ar", "22050", "-ac", "1",
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wav_path
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]
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subprocess.run(
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ffmpeg_cmd, check=True,
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stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,
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timeout=300,
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)
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has_audio = True
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except Exception as e:
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logger.warning(f"FFmpeg extraction failed: {e}")
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pass
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return {
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"url": f"/preview/{safe_name}{ext}",
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"audioUrl": f"/preview/{safe_name}.wav" if has_audio else f"/preview/{safe_name}{ext}",
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"filename": video.filename,
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}
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@router.get("/preview/{filename}")
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async def preview_serve(filename: str):
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if not filename or "/" in filename or "\\" in filename or filename.startswith("."):
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raise HTTPException(400, "Invalid preview filename")
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preview_real = os.path.realpath(PREVIEW_DIR)
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path = os.path.realpath(os.path.join(PREVIEW_DIR, filename))
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if not path.startswith(preview_real + os.sep):
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raise HTTPException(400, "Invalid preview filename")
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if not os.path.isfile(path):
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raise HTTPException(404, "Preview not found")
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ext = os.path.splitext(filename)[1].lower()
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media_types = {
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".mp4": "video/mp4", ".mov": "video/quicktime",
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".mkv": "video/x-matroska", ".webm": "video/webm",
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".avi": "video/x-msvideo", ".wav": "audio/wav",
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".mp3": "audio/mpeg"
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}
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return FileResponse(path, media_type=media_types.get(ext, "application/octet-stream"))
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# ── Legacy aliases for the extracted ingest pipeline (Phase 2.4 finish) ────
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_run_proc_factory = dub_pipeline.run_proc_factory
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_yt_download_sync = dub_pipeline.yt_download_sync
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_prep_event = dub_pipeline.prep_event
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_ingest_gen = dub_pipeline.ingest_pipeline
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@router.post("/dub/upload")
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async def dub_upload(video: UploadFile = File(...), job_id: Optional[str] = Form(None)):
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"""Accept video upload, write to disk, queue background prep task.
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Returns 202 with {job_id, task_id, filename}. Client should open SSE on
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/tasks/stream/{task_id} to monitor extract/demucs/scene stages and wait for
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the 'ready' event before starting transcription.
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"""
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job_id = job_id or str(uuid.uuid4())[:8]
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job_dir = _safe_job_dir(job_id)
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if job_dir is None:
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raise HTTPException(
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status_code=400,
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detail="Invalid job_id. Must be alphanumeric + hyphens/underscores only, ≤64 chars. Generate a fresh job_id or omit it to auto-create one.",
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)
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os.makedirs(job_dir, exist_ok=True)
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ext = os.path.splitext(video.filename or "video.mp4")[1]
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video_path = os.path.join(job_dir, f"original{ext}")
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with open(video_path, "wb") as f:
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f.write(await video.read())
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filename = video.filename or f"video{ext}"
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task_id = f"prep_{job_id}"
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await task_manager.add_task(
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task_id, "prep",
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_ingest_gen, job_id, job_dir,
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{"kind": "file", "path": video_path}, filename,
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)
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return JSONResponse(
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status_code=202,
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content={"job_id": job_id, "task_id": task_id, "filename": filename},
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)
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@router.post("/dub/ingest-url")
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async def dub_ingest_url(req: DubIngestUrlRequest):
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"""Ingest a remote video URL via yt-dlp. Queues background prep task.
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Returns 202 immediately with {job_id, task_id}. All work (download,
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audio extract, Demucs, scene detect, thumbnail) happens in the background
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task and progress is streamed via /tasks/stream/{task_id}.
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"""
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url = (req.url or "").strip()
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if not url or not (url.startswith("http://") or url.startswith("https://")):
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raise HTTPException(
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status_code=400,
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detail="URL must start with http:// or https://. Paste a full video link (e.g. https://youtube.com/watch?v=…) or drop a local file instead.",
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)
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try:
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import yt_dlp # noqa: F401
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except ImportError:
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raise HTTPException(
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status_code=500,
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detail="URL ingest needs yt-dlp, but it isn't installed. Install it (`pip install yt-dlp`) and restart the server — or drop a local video file instead.",
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)
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job_id = req.job_id or str(uuid.uuid4())[:8]
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job_dir = _safe_job_dir(job_id)
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if job_dir is None:
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raise HTTPException(
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status_code=400,
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detail="Invalid job_id. Must be alphanumeric + hyphens/underscores only, ≤64 chars. Generate a fresh job_id or omit it to auto-create one.",
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)
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os.makedirs(job_dir, exist_ok=True)
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task_id = f"prep_{job_id}"
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source = {
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"kind": "url",
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"url": url,
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"fetch_subs": bool(req.fetch_subs),
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"sub_langs": req.sub_langs or None,
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}
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await task_manager.add_task(
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task_id, "prep",
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_ingest_gen, job_id, job_dir,
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source, None,
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)
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return JSONResponse(
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status_code=202,
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content={"job_id": job_id, "task_id": task_id, "filename": ""},
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)
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TRANSCRIBE_CHUNK_S = float(os.environ.get("OMNIVOICE_TRANSCRIBE_CHUNK_S", "30.0"))
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_sse_event = dub_pipeline.sse_event
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_prep_event_helper = dub_pipeline.prep_event # alias; we keep the module-local _prep_event below for the inline one-liner shape
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@router.get("/dub/transcribe-stream/{job_id}")
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async def dub_transcribe_stream(job_id: str):
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"""Stream per-chunk segments via SSE, then emit diarized final pass."""
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job = _get_job(job_id)
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if not job:
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raise HTTPException(status_code=404, detail="Job not found")
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_model = await get_model()
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if _model._asr_pipe is None:
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raise HTTPException(
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status_code=503,
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detail="ASR (speech recognition) isn't loaded yet. The model is still warming up or Whisper failed to initialise — check Settings → Models for status, or restart the server if 'Idle' persists.",
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)
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asr_audio_target = job.get("vocals_path")
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if not asr_audio_target or not os.path.exists(asr_audio_target):
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asr_audio_target = job.get("audio_path")
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if not asr_audio_target or not os.path.exists(asr_audio_target):
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raise HTTPException(status_code=404, detail="No audio available for transcription")
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# ASR routing now goes through services.asr_backend so the active engine
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# (WhisperX by default, with faster-whisper / mlx / pytorch fallbacks) is
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# used consistently across platforms. dub pipelines specifically benefit
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# from WhisperX's wav2vec2 alignment (±10-30 ms word timing vs Whisper's
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# ±100-300 ms) — critical for lip-sync quality.
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from services.asr_backend import get_active_asr_backend
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_asr_backend = get_active_asr_backend(asr_pipe=getattr(_model, "_asr_pipe", None))
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scene_cuts = job.get("scene_cuts") or []
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async def gen():
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import math
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import tempfile
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loop = asyncio.get_event_loop()
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def _load():
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audio_np, sr = sf.read(asr_audio_target, dtype="float32")
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if audio_np.ndim > 1:
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audio_np = audio_np.mean(axis=1)
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return audio_np, sr
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try:
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audio_np, sr = await loop.run_in_executor(_cpu_pool, _load)
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except Exception as e:
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yield _sse_event("error", {"detail": f"audio load failed: {e}"})
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return
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total = float(len(audio_np)) / float(sr) if sr else 0.0
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chunks_n = max(1, int(math.ceil(total / TRANSCRIBE_CHUNK_S))) if total > 0 else 1
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yield _sse_event("start", {"duration": total, "chunks": chunks_n, "chunk_s": TRANSCRIBE_CHUNK_S})
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all_segments: list[dict] = []
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detected_lang = None
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next_seg_id = 0
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chunk_errors: list[str] = []
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for i in range(chunks_n):
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if job.get("aborted"):
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yield _sse_event("aborted", {})
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return
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t0 = i * TRANSCRIBE_CHUNK_S
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t1 = min(total, t0 + TRANSCRIBE_CHUNK_S)
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s_from = int(t0 * sr)
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s_to = int(t1 * sr)
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chunk_arr = audio_np[s_from:s_to]
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if len(chunk_arr) == 0:
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continue
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def _transcribe_chunk(arr=chunk_arr, offset=t0, local_sr=sr):
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# Route through the active backend (WhisperX by default).
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# Backends all take a file path, so write the chunk first.
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try:
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tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
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tmp.close()
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try:
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sf.write(tmp.name, arr, local_sr)
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r = _asr_backend.transcribe(tmp.name, word_timestamps=True)
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finally:
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try: os.remove(tmp.name)
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except OSError: pass
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shifted = []
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for c in r.get("chunks", []) or []:
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ts = c.get("timestamp", (0.0, 0.0)) or (0.0, 0.0)
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a0 = (ts[0] if ts[0] is not None else 0.0) + offset
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a1 = (ts[1] if ts[1] is not None else 0.0) + offset
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shifted.append({"text": c.get("text", ""), "timestamp": (a0, a1)})
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return {"chunks": shifted, "language": r.get("language")}
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except Exception as e:
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logger.exception("chunk transcribe failed (backend=%s)", _asr_backend.id)
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return {"chunks": [], "language": None, "error": str(e)}
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part = await loop.run_in_executor(_gpu_pool, _transcribe_chunk)
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if part.get("error"):
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chunk_errors.append(part["error"])
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if detected_lang is None and part.get("language"):
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detected_lang = part["language"]
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chunk_segs = segment_transcript(part, duration=t1, scene_cuts=scene_cuts)
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chunk_segs = assign_speakers_heuristic(chunk_segs)
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# Note: the Netflix subtitle CPS splitter (`segment_for_subtitles`)
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# used to run here but it's a *reading-speed* rule (17 CPS ceiling)
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# masquerading as segmentation. Normal speech runs 15–25 CPS; the
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# rule fired on every sentence and recursed to word-level. For
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# dubbing we keep the sentence-level output from segment_transcript;
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# if Netflix-compliant SRT is needed, apply segment_for_subtitles
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# inside the SRT export endpoint instead.
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for s in chunk_segs:
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s["id"] = f"s{next_seg_id:05x}"
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# Preserve pristine transcript so later translations can re-run from source
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# instead of compounding on previously-translated text.
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s["text_original"] = s.get("text", "")
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next_seg_id += 1
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all_segments.extend(chunk_segs)
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yield _sse_event("segments", {
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"chunk": i, "total_chunks": chunks_n,
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"segments": chunk_segs,
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"progress": (i + 1) / chunks_n,
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"error": part.get("error"),
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})
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if job.get("aborted"):
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yield _sse_event("aborted", {})
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return
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# Empty-transcription guard: if every chunk came back with zero
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# segments we can't proceed to diarization/clone extraction. Emit an
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# actionable error so the UI can surface a Retry instead of silently
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# landing in an empty editor. Commonly caused by a first-run model
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# download failure, a PyTorch 2.6 weights_only regression inside
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# whisperx's VAD load, or an unsupported audio format.
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if not all_segments:
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# Deduplicate while preserving order so one root cause doesn't
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# repeat N times in the UI toast.
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seen = set()
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uniq: list[str] = []
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for msg in chunk_errors:
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if msg and msg not in seen:
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seen.add(msg)
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uniq.append(msg)
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if uniq:
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detail = "Transcription produced no segments. " + " | ".join(uniq[:3])
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else:
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detail = (
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"Transcription produced no segments. The audio may be silent, "
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"too short, or in an unsupported format. Try re-uploading or "
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"check that the source has an audible speech track."
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)
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logger.error("transcribe yielded 0 segments (job=%s): %s", job_id, detail)
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||
yield _sse_event("error", {"detail": detail, "retryable": True})
|
||
yield _sse_event("done", {})
|
||
return
|
||
|
||
def _diarize():
|
||
diar_pipe = get_diarization_pipeline()
|
||
try:
|
||
if diar_pipe:
|
||
diar = diar_pipe(asr_audio_target)
|
||
return assign_speakers_from_diarization(all_segments, diar)
|
||
except Exception as e:
|
||
logger.error(f"Diarization failed: {e}")
|
||
return assign_speakers_heuristic(all_segments)
|
||
|
||
final_segs = await loop.run_in_executor(_gpu_pool, _diarize)
|
||
job["segments"] = final_segs
|
||
|
||
# Auto-speaker-clone: sample each detected speaker's voice from the
|
||
# Demucs-isolated vocals track and assign `auto:speaker_N` as the
|
||
# default profile for their segments. This is what lets a user add a
|
||
# new target language and have the ORIGINAL speaker speak it — the
|
||
# central pro-grade dubbing promise.
|
||
try:
|
||
from services.speaker_clone import extract_speaker_clones, auto_profile_id
|
||
vocals_for_clone = job.get("vocals_path") or asr_audio_target
|
||
clones = await loop.run_in_executor(
|
||
_cpu_pool, extract_speaker_clones,
|
||
vocals_for_clone, final_segs, os.path.dirname(vocals_for_clone),
|
||
)
|
||
if clones:
|
||
job["speaker_clones"] = clones
|
||
# Default each segment's profile_id to its speaker's auto-clone,
|
||
# but only if the user hasn't already assigned something.
|
||
for s in final_segs:
|
||
if s.get("profile_id"):
|
||
continue
|
||
spk = s.get("speaker_id") or "Speaker 1"
|
||
if spk in clones:
|
||
s["profile_id"] = auto_profile_id(spk)
|
||
except Exception as e:
|
||
logger.warning("speaker_clone extraction skipped: %s", e)
|
||
|
||
job["source_lang"] = ((detected_lang or "en").split("_")[0][:2] or "en").lower()
|
||
job["full_transcript"] = " ".join(s.get("text", "") for s in final_segs)
|
||
_save_job(job_id, job)
|
||
|
||
if torch.backends.mps.is_available():
|
||
try: torch.mps.empty_cache()
|
||
except Exception: pass
|
||
|
||
yield _sse_event("final", {
|
||
"segments": final_segs,
|
||
"source_lang": job["source_lang"],
|
||
"full_transcript": job["full_transcript"],
|
||
"speaker_clones": job.get("speaker_clones", {}),
|
||
})
|
||
yield _sse_event("done", {})
|
||
|
||
return StreamingResponse(
|
||
gen(),
|
||
media_type="text/event-stream",
|
||
headers={
|
||
"Cache-Control": "no-cache, no-transform",
|
||
"X-Accel-Buffering": "no",
|
||
},
|
||
)
|
||
|
||
|
||
@router.post("/dub/transcribe/{job_id}")
|
||
async def dub_transcribe(job_id: str):
|
||
job = _get_job(job_id)
|
||
if not job:
|
||
raise HTTPException(status_code=404, detail="Job not found")
|
||
_model = await get_model()
|
||
if _model._asr_pipe is None:
|
||
raise HTTPException(
|
||
status_code=503,
|
||
detail="ASR (speech recognition) isn't loaded yet. The model is still warming up or Whisper failed to initialise — check Settings → Models for status, or restart the server if 'Idle' persists.",
|
||
)
|
||
|
||
def _transcribe():
|
||
import re
|
||
import traceback
|
||
|
||
asr_audio_target = job.get("vocals_path")
|
||
if not asr_audio_target or not os.path.exists(asr_audio_target):
|
||
asr_audio_target = job.get("audio_path")
|
||
|
||
import torch
|
||
|
||
detected_lang = None
|
||
|
||
# Route through services.asr_backend — picks WhisperX / faster-whisper
|
||
# / mlx / pytorch based on what's installed + user preference. Works
|
||
# identically on all platforms; the older mlx-vs-pytorch branching
|
||
# here duplicated the logic in asr_backend.py and skipped WhisperX.
|
||
from services.asr_backend import get_active_asr_backend
|
||
_asr = get_active_asr_backend(asr_pipe=getattr(_model, "_asr_pipe", None))
|
||
try:
|
||
logger.info("Transcribing full audio via %s ...", _asr.id)
|
||
result = _asr.transcribe(asr_audio_target, word_timestamps=True)
|
||
detected_lang = result.get("language")
|
||
except Exception as e:
|
||
logger.error("ASR backend %s failed: %s", _asr.id, e)
|
||
# Last-resort fallback — in-memory pytorch whisper via the TTS
|
||
# model's pipeline. Guaranteed present since the TTS model is
|
||
# already loaded to reach this code path.
|
||
audio_np, sr = sf.read(asr_audio_target, dtype="float32")
|
||
if audio_np.ndim > 1: audio_np = audio_np.mean(axis=1)
|
||
bs = 16 if torch.cuda.is_available() else 1
|
||
result = _model._asr_pipe(
|
||
{"array": audio_np, "sampling_rate": sr},
|
||
return_timestamps=True, chunk_length_s=15, batch_size=bs,
|
||
)
|
||
detected_lang = (result.get("language") if isinstance(result, dict) else None)
|
||
|
||
job["source_lang"] = (detected_lang or "en").split("_")[0][:2].lower()
|
||
|
||
scene_cuts = job.get("scene_cuts") or []
|
||
segments = segment_transcript(result, duration=job.get("duration", 0.0), scene_cuts=scene_cuts)
|
||
|
||
diar_pipe = get_diarization_pipeline()
|
||
if diar_pipe:
|
||
try:
|
||
diar_target = job.get("vocals_path") or job.get("audio_path")
|
||
diarization = diar_pipe(diar_target)
|
||
segments = assign_speakers_from_diarization(segments, diarization)
|
||
except Exception as e:
|
||
logger.error(f"Pyannote diarization failed during inference: {e}. Falling back to heuristic.")
|
||
segments = assign_speakers_heuristic(segments)
|
||
else:
|
||
segments = assign_speakers_heuristic(segments)
|
||
|
||
# Previously ran `segment_for_subtitles(segments)` here. Removed 2026-04-21 —
|
||
# that splitter enforces Netflix's 17 CPS reading-speed ceiling which
|
||
# trips on normal speech (15–25 CPS) and recurses to word-level.
|
||
# For dubbing, keep the sentence-level output. Apply subtitle rules at
|
||
# SRT export time only.
|
||
|
||
for s in segments:
|
||
s.setdefault("text_original", s.get("text", ""))
|
||
job["full_transcript"] = " ".join(s["text"] for s in segments)
|
||
|
||
if torch.backends.mps.is_available():
|
||
torch.mps.empty_cache()
|
||
|
||
return segments
|
||
|
||
try:
|
||
loop = asyncio.get_event_loop()
|
||
try:
|
||
segments_result = await loop.run_in_executor(_gpu_pool, _transcribe)
|
||
except asyncio.CancelledError:
|
||
job["aborted"] = True
|
||
raise
|
||
if job.get("aborted"):
|
||
raise HTTPException(status_code=499, detail="Transcription aborted")
|
||
job["segments"] = segments_result
|
||
source_lang = job.get("source_lang")
|
||
_save_job(job_id, job)
|
||
return {
|
||
"job_id": job_id,
|
||
"segments": segments_result,
|
||
"full_transcript": job.get("full_transcript", ""),
|
||
"source_lang": source_lang,
|
||
}
|
||
except HTTPException:
|
||
raise
|
||
except asyncio.CancelledError:
|
||
raise
|
||
except Exception as e:
|
||
import traceback
|
||
traceback.print_exc()
|
||
raise HTTPException(status_code=500, detail=str(e))
|