Upload app.py

app.py

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+import os
+import time
+import uuid
+import asyncio
+import tempfile
+import shutil
+import threading
+import logging
+import torch
+import torchaudio
+from fastapi import FastAPI, Request, HTTPException, BackgroundTasks
+
+
+class HealthAndStatusFilter(logging.Filter):
+    """جلوی چاپ لاگ‌های /status/ و /health رو می‌گیره تا کنسول شلوغ نشه (خودِ درخواست‌ها عادی پردازش میشن)"""
+    def filter(self, record):
+        message = record.getMessage()
+        return "/status/" not in message and "/health" not in message
+
+
+logging.getLogger("uvicorn.access").addFilter(HealthAndStatusFilter())
+from fastapi.middleware.cors import CORSMiddleware
+from faster_whisper import WhisperModel
+from df.enhance import enhance, init_df, load_audio, save_audio
+from pyannote.audio import Pipeline as DiarizationPipeline
+from pipeline.postprocess import process
+
+torch.set_num_threads(2)  # جلوی رقابت بی‌فایده‌ی رشته‌ها روی CPU محدود رو می‌گیره
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+print("Loading Whisper model...", flush=True)
+model = WhisperModel("large-v3", device="cpu", compute_type="int8")
+print("Whisper model ready!")
+
+print("Loading DeepFilterNet model...", flush=True)
+df_model, df_state, _ = init_df()
+print("DeepFilterNet model ready!")
+
+USE_DEEPFILTERNET = False  # غیرفعال - شواهد نشون داد کمکی نمی‌کنه و بعضی صداها رو خراب می‌کنه
+
+print("Loading word-alignment model...", flush=True)
+align_bundle = torchaudio.pipelines.MMS_FA
+align_model = align_bundle.get_model().to("cpu")
+align_labels = align_bundle.get_labels()
+align_dictionary = {c: i for i, c in enumerate(align_labels)}
+print("Word-alignment model ready!")
+
+print("Loading speaker-diarization model...", flush=True)
+HF_TOKEN = os.environ.get("HF_TOKEN")
+diarize_pipeline = DiarizationPipeline.from_pretrained(
+    "pyannote/speaker-diarization-3.1", use_auth_token=HF_TOKEN
+)
+print("Speaker-diarization model ready!")
+
+MIN_NOISE_BLOCK_SEC = 1.0
+JOB_TTL_SECONDS = 30 * 60  # کارهای قدیمی بعد از ۳۰ دقیقه پاک میشن
+STAGE_TIMEOUT_SEC = 240  # سقف زمانی برای هر مرحله‌ی سنگین
+
+# ─── نگهداری وضعیت کارها (Jobs) توی حافظه ───────────────────────────────────
+jobs = {}
+jobs_lock = threading.Lock()
+
+
+def new_job():
+    job_id = uuid.uuid4().hex
+    tmp_dir = tempfile.mkdtemp(prefix=f"subflow_{job_id}_")
+    with jobs_lock:
+        jobs[job_id] = {
+            "status": "uploading",
+            "detail": "در انتظار دریافت فایل...",
+            "created_at": time.time(),
+            "tmp_dir": tmp_dir,
+            "chunk_paths": [],
+            "srt": None,
+            "language": None,
+            "duration": None,
+            "error": None,
+        }
+    return job_id
+
+
+def get_job(job_id):
+    with jobs_lock:
+        job = jobs.get(job_id)
+    if job is None:
+        raise HTTPException(status_code=404, detail="این کار پیدا نشد یا منقضی شده")
+    return job
+
+
+def update_job(job_id, **kwargs):
+    with jobs_lock:
+        if job_id in jobs:
+            jobs[job_id].update(kwargs)
+
+
+def cleanup_expired_jobs():
+    now = time.time()
+    with jobs_lock:
+        expired = [jid for jid, j in jobs.items() if now - j["created_at"] > JOB_TTL_SECONDS]
+    for jid in expired:
+        with jobs_lock:
+            job = jobs.pop(jid, None)
+        if job and os.path.isdir(job["tmp_dir"]):
+            shutil.rmtree(job["tmp_dir"], ignore_errors=True)
+        print(f"[cleanup] removed expired job {jid}")
+
+
+# ─── منطق تشخیص گوینده و هم‌ترازی (بدون تغییر نسبت به نسخه‌ی قبلی) ──────────
+
+def build_speaker_lookup(diarization):
+    """
+    لیست بازه‌های گوینده‌ها رو یه‌بار مرتب می‌کنه تا جست‌وجوی «این لحظه مال کدوم
+    گوینده‌ست» برای هر جمله، به‌جای گشتن کامل لیست، سریع (با جست‌وجوی دودویی) انجام بشه.
+    """
+    turns = sorted(
+        [(turn.start, turn.end, speaker) for turn, _, speaker in diarization.itertracks(yield_label=True)],
+        key=lambda x: x[0],
+    )
+    return turns
+
+
+def get_speaker_at(turns, time_sec):
+    import bisect
+    starts = [t[0] for t in turns]
+    idx = bisect.bisect_right(starts, time_sec) - 1
+    if idx >= 0:
+        start, end, speaker = turns[idx]
+        if start <= time_sec <= end:
+            return speaker
+    return None
+
+
+def get_speaker_for_segment(turns, seg_start, seg_end):
+    """
+    به‌جای اعتماد به یه لحظه‌ی تنها، چند نقطه در طول جمله رو چک می‌کنه
+    و گوینده‌ای که بیشترین رأی رو آورده برمی‌گردونه.
+    """
+    duration = seg_end - seg_start
+    if duration <= 0:
+        return get_speaker_at(turns, seg_start)
+
+    sample_points = [seg_start + duration * frac for frac in (0.2, 0.4, 0.5, 0.6, 0.8)]
+    votes = {}
+    for t in sample_points:
+        speaker = get_speaker_at(turns, t)
+        if speaker is not None:
+            votes[speaker] = votes.get(speaker, 0) + 1
+
+    if not votes:
+        return None
+    return max(votes, key=votes.get)
+
+
+def find_leading_noise_cutoff(turns, whisper_start, search_back_sec=4.0):
+    STABLE_SPEAKER_SEC = 0.2
+    window_start = max(0, whisper_start - search_back_sec)
+    step = 0.05
+    t = whisper_start
+    noise_end = None
+    noise_start = None
+    speaker_run_start = None
+
+    while t > window_start:
+        speaker = get_speaker_at(turns, t)
+
+        if speaker is None:
+            speaker_run_start = None
+            if noise_end is None:
+                noise_end = t
+            noise_start = t
+        else:
+            if speaker_run_start is None:
+                speaker_run_start = t
+            run_len = speaker_run_start - t
+            if run_len >= STABLE_SPEAKER_SEC:
+                break
+            if noise_end is None:
+                noise_end = t
+            noise_start = t
+
+        t -= step
+
+    if noise_start is not None and noise_end is not None:
+        block_len = noise_end - noise_start
+        if block_len >= MIN_NOISE_BLOCK_SEC:
+            return noise_end
+    return None
+
+
+def realign_word_timestamps(waveform_16k, words, whisper_start, whisper_end, cutoff_start=None):
+    try:
+        margin = 0.5
+        raw_start = whisper_start - margin
+        effective_start = max(raw_start, cutoff_start) if cutoff_start is not None else raw_start
+
+        start_sample = max(0, int(effective_start * 16000))
+        end_sample = int((whisper_end + margin) * 16000)
+        clip = waveform_16k[:, start_sample:end_sample]
+
+        clean_words = []
+        for w in words:
+            token = "".join(ch for ch in w["word"].lower() if ch in align_dictionary)
+            if token:
+                clean_words.append((w, token))
+
+        if not clean_words:
+            return words
+
+        transcript = "".join(tok for _, tok in clean_words)
+        tokens = [align_dictionary[c] for c in transcript]
+
+        with torch.inference_mode():
+            emission, _ = align_model(clip)
+
+        targets = torch.tensor([tokens], dtype=torch.int32)
+        input_lengths = torch.tensor([emission.shape[1]])
+        target_lengths = torch.tensor([len(tokens)])
+
+        aligned_tokens, scores = torchaudio.functional.forced_align(
+            emission, targets, input_lengths, target_lengths, blank=0
+        )
+        spans = torchaudio.functional.merge_tokens(aligned_tokens[0], scores[0])
+
+        ratio = clip.shape[1] / emission.shape[1] / 16000
+        new_words = []
+        span_idx = 0
+        for w, tok in clean_words:
+            n_chars = len(tok)
+            matched = [s for s in spans[span_idx:] if s.token != 0][:n_chars]
+            if len(matched) < n_chars:
+                new_words.append(w)
+                continue
+            w_start = matched[0].start * ratio + effective_start
+            w_end = matched[-1].end * ratio + effective_start
+            new_words.append({"word": w["word"], "start": round(max(0, w_start), 3), "end": round(max(0, w_end), 3)})
+            span_idx += n_chars
+
+        return new_words if len(new_words) == len(words) else words
+    except Exception as e:
+        print(f"  [align] skipped due to: {type(e).__name__}: {e}")
+        return words
+
+
+def run_transcription(audio_path):
+    return model.transcribe(
+        audio_path,
+        language="en",
+        word_timestamps=True,
+        vad_filter=True,
+        vad_parameters=dict(
+            min_silence_duration_ms=300,
+            speech_pad_ms=200,
+            threshold=0.3,
+        ),
+    )
+
+
+def run_diarization(audio_path):
+    return diarize_pipeline(audio_path, max_speakers=8)
+
+
+def run_alignment_pass(segments_raw, align_waveform, diarization):
+    turns = build_speaker_lookup(diarization)
+    segments = []
+    for seg in segments_raw:
+        words = list(seg.words)
+        if not words:
+            continue
+        word_dicts = [{"word": w.word, "start": w.start, "end": w.end} for w in words]
+
+        cutoff = find_leading_noise_cutoff(turns, word_dicts[0]["start"])
+        if cutoff is not None:
+            print(f"  [noise-trim] '{seg.text.strip()[:40]}...' cutoff moved from {word_dicts[0]['start']:.2f}s to {cutoff:.2f}s")
+
+        realigned = realign_word_timestamps(
+            align_waveform, word_dicts, word_dicts[0]["start"], word_dicts[-1]["end"], cutoff_start=cutoff
+        )
+
+        speaker = get_speaker_for_segment(turns, realigned[0]["start"], realigned[-1]["end"])
+
+        segments.append({
+            "text": seg.text.strip(),
+            "start": realigned[0]["start"],
+            "end": realigned[-1]["end"],
+            "words": realigned,
+            "speaker": speaker,
+        })
+    return segments
+
+
+class ProcessingTimeout(Exception):
+    pass
+
+
+def run_blocking_with_timeout(job_id, blocking_fn, *args, status, detail, max_seconds=STAGE_TIMEOUT_SEC):
+    """
+    نسخه‌ی هم‌زمان (نه async) - چون این خودش داخل یه thread پس‌زمینه اجرا میشه،
+    نیازی به هماهنگی با event loop نداره. وضعیت Job رو مستقیم آپدیت می‌کنه
+    تا polling بتونه ببینتش.
+    """
+    result_holder = {}
+    error_holder = {}
+
+    def runner():
+        try:
+            result_holder["value"] = blocking_fn(*args)
+        except Exception as e:
+            error_holder["error"] = e
+
+    update_job(job_id, status=status, detail=detail)
+    t = threading.Thread(target=runner, daemon=True)
+    t.start()
+
+    elapsed = 0
+    while t.is_alive():
+        t.join(timeout=3)
+        if t.is_alive():
+            elapsed += 3
+            if elapsed >= max_seconds:
+                raise ProcessingTimeout(f"مرحله‌ی «{detail}» بیشتر از {max_seconds} ثانیه طول کشید و متوقف شد.")
+            update_job(job_id, status=status, detail=f"{detail} ({elapsed} ثانیه)")
+
+    if "error" in error_holder:
+        raise error_holder["error"]
+    return result_holder.get("value")
+
+
+def process_job(job_id):
+    """کل پایپلاین پردازش - این تابع توی یه thread پس‌زمینه اجرا میشه، نه توی درخواست HTTP."""
+    job = get_job(job_id)
+    tmp_dir = job["tmp_dir"]
+    tmp_path = os.path.join(tmp_dir, "input.wav")
+    enhanced_path = None
+
+    try:
+        with open(tmp_path, "wb") as f:
+            for chunk_path in job["chunk_paths"]:
+                with open(chunk_path, "rb") as c:
+                    f.write(c.read())
+        print(f"Job {job_id}: assembled {len(job['chunk_paths'])} chunks into {tmp_path}")
+
+        audio_to_transcribe = tmp_path
+
+        if USE_DEEPFILTERNET:
+            update_job(job_id, status="cleaning", detail="در حال حذف نویز پس‌زمینه")
+            audio_data, _ = load_audio(tmp_path, sr=df_state.sr())
+            enhanced_audio = enhance(df_model, df_state, audio_data, atten_lim_db=20)
+            enhanced_path = os.path.join(tmp_dir, "enhanced.wav")
+            save_audio(enhanced_path, enhanced_audio, df_state.sr())
+            audio_to_transcribe = enhanced_path
+
+        segments_raw, info = run_blocking_with_timeout(
+            job_id, run_transcription, audio_to_transcribe,
+            status="transcribing", detail="مدل large-v3 در حال شنیدن صداست",
+        )
+        update_job(job_id, detail="تشخیص گفتار کامل شد")
+
+        align_waveform, sr = torchaudio.load(audio_to_transcribe)
+        if sr != 16000:
+            align_waveform = torchaudio.functional.resample(align_waveform, sr, 16000)
+        if align_waveform.shape[0] > 1:
+            align_waveform = align_waveform.mean(dim=0, keepdim=True)
+
+        diarization = run_blocking_with_timeout(
+            job_id, run_diarization, audio_to_transcribe,
+            status="aligning", detail="در حال تشخیص گوینده‌ها",
+        )
+        update_job(job_id, detail="گوینده‌ها شناسایی شدند")
+
+        segments_raw_list = list(segments_raw)
+        segments = run_blocking_with_timeout(
+            job_id, run_alignment_pass, segments_raw_list, align_waveform, diarization,
+            status="aligning", detail=f"در حال هماهنگ‌سازی زمان‌بندی {len(segments_raw_list)} جمله",
+        )
+        update_job(job_id, detail=f"{len(segments)} خط شناسایی شد")
+
+        print(f"Job {job_id}: got {len(segments)} segments")
+        for i, seg in enumerate(segments):
+            print(f"  seg[{i}]: '{seg['text']}' | speaker={seg['speaker']} | dur={seg['end']-seg['start']:.2f}s")
+
+        update_job(job_id, status="processing", detail=f"در حال چیدمان {len(segments)} خط زیرنویس")
+        srt_content = process(segments)
+        update_job(job_id, detail="زیرنویس ساخته شد")
+
+        update_job(
+            job_id,
+            status="done",
+            detail="زیرنویس آماده شد",
+            srt=srt_content,
+            language=info.language,
+            duration=info.duration,
+        )
+        print(f"Job {job_id}: done!")
+
+    except ProcessingTimeout as e:
+        print(f"Job {job_id}: TIMEOUT: {e}")
+        update_job(job_id, status="error", error=str(e))
+    except Exception as e:
+        print(f"Job {job_id}: ERROR: {type(e).__name__}: {e}")
+        import traceback
+        traceback.print_exc()
+        update_job(job_id, status="error", error=str(e))
+    finally:
+        cleanup_expired_jobs()
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "message": "SubFlow backend is running"}
+
+
+@app.post("/upload/start")
+async def upload_start():
+    cleanup_expired_jobs()
+    job_id = new_job()
+    return {"job_id": job_id}
+
+
+@app.post("/upload/chunk/{job_id}")
+async def upload_chunk(job_id: str, request: Request):
+    job = get_job(job_id)
+    chunk_bytes = await request.body()
+    chunk_index = len(job["chunk_paths"])
+    chunk_path = os.path.join(job["tmp_dir"], f"chunk_{chunk_index:06d}.bin")
+    with open(chunk_path, "wb") as f:
+        f.write(chunk_bytes)
+    with jobs_lock:
+        jobs[job_id]["chunk_paths"].append(chunk_path)
+    return {"received": chunk_index}
+
+
+@app.post("/upload/finish/{job_id}")
+async def upload_finish(job_id: str, background_tasks: BackgroundTasks):
+    job = get_job(job_id)
+    total_bytes = sum(os.path.getsize(p) for p in job["chunk_paths"])
+    print(f"Job {job_id}: upload finished, {len(job['chunk_paths'])} chunks, {total_bytes} bytes total")
+    update_job(job_id, status="receiving", detail="فایل آماده‌ی پردازش شد")
+    background_tasks.add_task(process_job, job_id)
+    return {"status": "processing_started"}
+
+
+@app.get("/status/{job_id}")
+async def status(job_id: str):
+    job = get_job(job_id)
+    response = {
+        "status": job["status"],
+        "detail": job["detail"],
+    }
+    if job["status"] == "done":
+        response["srt"] = job["srt"]
+        response["language"] = job["language"]
+        response["duration"] = job["duration"]
+    elif job["status"] == "error":
+        response["message"] = job["error"]
+    return response