Delete app.py

app.py

@@ -1,291 +0,0 @@
-import os
-import tempfile
-import torch
-import torchaudio
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
-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
-
-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  # حداقل طول یه بلوک بی‌گوینده تا «نویز» حساب بشه
-
-
-def get_speaker_at(diarization, time_sec):
-    for turn, _, speaker in diarization.itertracks(yield_label=True):
-        if turn.start <= time_sec <= turn.end:
-            return speaker
-    return None
-
-
-def find_leading_noise_cutoff(diarization, whisper_start, search_back_sec=4.0):
-    """
-    برمی‌گرده به عقب از شروعِ حدسیِ Whisper، دنبال یه بلوک نویز/بی‌گوینده‌ی
-    طولانی (حداقل MIN_NOISE_BLOCK_SEC) که دقیقاً قبل از این جمله باشه.
-    نوسان‌های خیلی کوتاه (کمتر از STABLE_SPEAKER_SEC) که یه گوینده برای یه
-    لحظه‌ی گذرا ظاهر میشه رو نادیده می‌گیره - فقط با دیدنِ یه گوینده‌ی
-    "پایدار" واقعاً متوقف میشه.
-    اگه پیدا کرد، زمان پایانِ اون بلوک نویز رو برمی‌گردونه (یعنی «از اینجا به بعد
-    اعتماد کن»)؛ اگه پیدا نکرد، None برمی‌گردونه (یعنی چیزی رو نبُر).
-    """
-    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(diarization, 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
-            # نوسان کوتاه‌مدت - نادیده بگیر و بخشی از بلوک نویز حساب کن
-            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):
-    """
-    برای یه جمله، به‌جای زمان‌بندی حدسی خودِ Whisper، با یه مدل تخصصی جدا
-    (wav2vec2 از طریق torchaudio) دقیقاً چک می‌کنه هر کلمه از کجای صدا گفته شده.
-    اگه cutoff_start داده بشه (یعنی یه بلوک نویز قبل از جمله شناسایی شده)،
-    جستجو از همون نقطه شروع میشه، نه از قبل‌ترش.
-    اگه به هر دلیلی هم‌ترازی جواب نداد، همون زمان‌بندی قبلی Whisper دست‌نخورده می‌مونه.
-    """
-    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
-
-
-@app.get("/health")
-async def health():
-    return {"status": "ok", "message": "SubFlow backend is running"}
-
-
-@app.websocket("/transcribe")
-async def transcribe(websocket: WebSocket):
-    await websocket.accept()
-    tmp_path = None
-    enhanced_path = None
-
-    try:
-        print("Step 1: waiting for bytes")
-        audio_bytes = await websocket.receive_bytes()
-        print(f"Step 2: received {len(audio_bytes)} bytes")
-        await websocket.send_json({"status": "receiving"})
-
-        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
-            tmp.write(audio_bytes)
-            tmp_path = tmp.name
-        print(f"Step 3: saved to {tmp_path}")
-
-        audio_to_transcribe = tmp_path
-
-        if USE_DEEPFILTERNET:
-            await websocket.send_json({"status": "cleaning"})
-            print("Step 3.5: cleaning audio with DeepFilterNet")
-
-            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 = tmp_path.replace(".wav", "_enhanced.wav")
-            save_audio(enhanced_path, enhanced_audio, df_state.sr())
-            audio_to_transcribe = enhanced_path
-            print(f"Step 3.6: cleaned audio saved to {enhanced_path}")
-        else:
-            print("Step 3.5: skipping DeepFilterNet (disabled for A/B test)")
-
-        await websocket.send_json({"status": "transcribing"})
-        print("Step 4: starting transcription")
-
-        segments_raw, info = model.transcribe(
-            audio_to_transcribe,
-            language="en",
-            word_timestamps=True,
-            vad_filter=True,
-            vad_parameters=dict(
-                min_silence_duration_ms=300,
-                speech_pad_ms=200,
-                threshold=0.3,
-            ),
-        )
-
-        print("Step 5: transcription done")
-
-        await websocket.send_json({"status": "aligning"})
-        print("Step 5.5: loading audio + running speaker diarization")
-
-        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 = diarize_pipeline(audio_to_transcribe)
-        print("Step 5.6: diarization done, now aligning words per segment")
-
-        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(diarization, 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
-            )
-
-            mid_time = (realigned[0]["start"] + realigned[-1]["end"]) / 2
-            speaker = get_speaker_at(diarization, mid_time)
-
-            segments.append({
-                "text": seg.text.strip(),
-                "start": realigned[0]["start"],
-                "end": realigned[-1]["end"],
-                "words": realigned,
-                "speaker": speaker,
-            })
-
-        print(f"Step 6: 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")
-
-        await websocket.send_json({"status": "processing"})
-
-        srt_content = process(segments)
-        print(f"Step 7: SRT built")
-
-        await websocket.send_json({
-            "status": "done",
-            "srt": srt_content,
-            "language": info.language,
-            "duration": info.duration,
-        })
-        print("Step 8: done sent!")
-        import asyncio
-        await asyncio.sleep(1)
-
-    except WebSocketDisconnect:
-        print("WebSocket disconnected by client")
-    except Exception as e:
-        print(f"ERROR: {type(e).__name__}: {e}")
-        import traceback
-        traceback.print_exc()
-        try:
-            await websocket.send_json({"status": "error", "message": str(e)})
-        except Exception:
-            pass
-    finally:
-        if tmp_path and os.path.exists(tmp_path):
-            os.remove(tmp_path)
-        if enhanced_path and os.path.exists(enhanced_path):
-            os.remove(enhanced_path)
-        try:
-            await websocket.close()
-        except Exception:
-            pass