33cca5f552
CutScript is a local-first, Descript-like video editor where you edit video by editing text. Delete a word from the transcript and it's cut from the video. Features: - Word-level transcription with WhisperX - Text-based video editing with undo/redo - AI filler word removal (Ollama/OpenAI/Claude) - AI clip creation for shorts - Waveform timeline with virtualized transcript - FFmpeg stream-copy (fast) and re-encode (4K) export - Caption burn-in and sidecar SRT generation - Studio Sound audio enhancement (DeepFilterNet) - Keyboard shortcuts (J/K/L, Space, Delete, Ctrl+Z/S/E) - Encrypted API key storage - Project save/load (.aive files) Architecture: - Electron + React + Tailwind (frontend) - FastAPI + Python (backend) - WhisperX for transcription - FFmpeg for video processing - Multi-provider AI support Performance optimizations: - RAF-throttled time updates - Zustand selectors for granular subscriptions - Dual-canvas waveform rendering - Virtualized transcript with react-virtuoso Built on top of DataAnts-AI/VideoTranscriber, completely rewritten as a desktop application. License: MIT
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
"""
|
|
Speaker diarization service using pyannote.audio.
|
|
Refactored from the original repo -- removed Streamlit dependency.
|
|
"""
|
|
|
|
import logging
|
|
import os
|
|
from pathlib import Path
|
|
from typing import Optional
|
|
|
|
import torch
|
|
|
|
from utils.gpu_utils import get_optimal_device
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
_pipeline_cache = {}
|
|
|
|
|
|
def _get_pipeline(hf_token: str, device: torch.device):
|
|
cache_key = str(device)
|
|
if cache_key in _pipeline_cache:
|
|
return _pipeline_cache[cache_key]
|
|
|
|
try:
|
|
from pyannote.audio import Pipeline
|
|
|
|
pipeline = Pipeline.from_pretrained(
|
|
"pyannote/speaker-diarization-3.0",
|
|
use_auth_token=hf_token,
|
|
)
|
|
if device.type == "cuda":
|
|
pipeline = pipeline.to(device)
|
|
|
|
_pipeline_cache[cache_key] = pipeline
|
|
return pipeline
|
|
except Exception as e:
|
|
logger.error(f"Failed to load diarization pipeline: {e}")
|
|
return None
|
|
|
|
|
|
def diarize_and_label(
|
|
transcription_result: dict,
|
|
audio_path: str,
|
|
hf_token: Optional[str] = None,
|
|
num_speakers: Optional[int] = None,
|
|
use_gpu: bool = True,
|
|
) -> dict:
|
|
"""
|
|
Apply speaker diarization to an existing transcription result.
|
|
Adds 'speaker' field to each word and segment.
|
|
|
|
Returns the mutated transcription_result with speaker labels.
|
|
"""
|
|
hf_token = hf_token or os.environ.get("HF_TOKEN")
|
|
if not hf_token:
|
|
logger.warning("No HuggingFace token provided; skipping diarization")
|
|
return transcription_result
|
|
|
|
device = get_optimal_device() if use_gpu else torch.device("cpu")
|
|
pipeline = _get_pipeline(hf_token, device)
|
|
if pipeline is None:
|
|
return transcription_result
|
|
|
|
audio_path = Path(audio_path)
|
|
logger.info(f"Running diarization on {audio_path}")
|
|
|
|
try:
|
|
diarization = pipeline(str(audio_path), num_speakers=num_speakers)
|
|
except Exception as e:
|
|
logger.error(f"Diarization failed: {e}")
|
|
return transcription_result
|
|
|
|
speaker_map = []
|
|
for turn, _, speaker in diarization.itertracks(yield_label=True):
|
|
speaker_map.append((turn.start, turn.end, speaker))
|
|
|
|
def _find_speaker(start: float, end: float) -> str:
|
|
best_overlap = 0
|
|
best_speaker = "UNKNOWN"
|
|
for s_start, s_end, speaker in speaker_map:
|
|
overlap_start = max(start, s_start)
|
|
overlap_end = min(end, s_end)
|
|
overlap = max(0, overlap_end - overlap_start)
|
|
if overlap > best_overlap:
|
|
best_overlap = overlap
|
|
best_speaker = speaker
|
|
return best_speaker
|
|
|
|
for word in transcription_result.get("words", []):
|
|
word["speaker"] = _find_speaker(word["start"], word["end"])
|
|
|
|
for segment in transcription_result.get("segments", []):
|
|
segment["speaker"] = _find_speaker(segment["start"], segment["end"])
|
|
for w in segment.get("words", []):
|
|
w["speaker"] = _find_speaker(w["start"], w["end"])
|
|
|
|
return transcription_result
|