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https://github.com/kuhyx/testsAndMisc.git
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feat: more descriptive transbtion installation
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@ -7,6 +7,121 @@ import sys
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import time
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from datetime import timedelta
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from typing import List, Optional
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def format_bytes(size: int) -> str:
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"""Format bytes as human-readable string."""
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for unit in ['B', 'KB', 'MB', 'GB']:
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if size < 1024:
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return f"{size:.1f}{unit}"
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size /= 1024
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return f"{size:.1f}TB"
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def download_model_with_progress(model_name: str) -> str:
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"""Download model files from HuggingFace with a visible progress bar.
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Returns the local path to the downloaded model.
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"""
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try:
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from huggingface_hub import snapshot_download, hf_hub_download
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from huggingface_hub.utils import EntryNotFoundError
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except ImportError:
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print("[WARN] huggingface_hub not available, falling back to default download", file=sys.stderr)
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return model_name
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# Map common model names to HF repo IDs
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model_map = {
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"tiny": "Systran/faster-whisper-tiny",
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"tiny.en": "Systran/faster-whisper-tiny.en",
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"base": "Systran/faster-whisper-base",
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"base.en": "Systran/faster-whisper-base.en",
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"small": "Systran/faster-whisper-small",
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"small.en": "Systran/faster-whisper-small.en",
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"medium": "Systran/faster-whisper-medium",
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"medium.en": "Systran/faster-whisper-medium.en",
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"large-v1": "Systran/faster-whisper-large-v1",
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"large-v2": "Systran/faster-whisper-large-v2",
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"large-v3": "Systran/faster-whisper-large-v3",
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"large": "Systran/faster-whisper-large-v3",
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"distil-large-v2": "Systran/faster-distil-whisper-large-v2",
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"distil-large-v3": "Systran/faster-distil-whisper-large-v3",
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"distil-medium.en": "Systran/faster-distil-whisper-medium.en",
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"distil-small.en": "Systran/faster-distil-whisper-small.en",
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}
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repo_id = model_map.get(model_name, model_name)
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# Check if it looks like a repo ID
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if "/" not in repo_id and model_name not in model_map:
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# Assume it's a Systran model
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repo_id = f"Systran/faster-whisper-{model_name}"
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print(f"[INFO] Checking model: {repo_id}", flush=True)
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# Files we need to download (model.bin is the large one)
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required_files = ["config.json", "model.bin", "tokenizer.json", "vocabulary.txt"]
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try:
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# Use snapshot_download which handles caching and shows what's happening
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# First, let's check if model.bin needs downloading by checking cache
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from huggingface_hub import try_to_load_from_cache, HfFileSystem
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cache_path = try_to_load_from_cache(repo_id, "model.bin")
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if cache_path is not None:
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print(f"[INFO] Model already cached, loading from: {os.path.dirname(cache_path)}", flush=True)
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# Return the directory containing the cached files
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return os.path.dirname(cache_path)
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# Model not cached, need to download
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print(f"[INFO] Downloading model files from {repo_id}...", flush=True)
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print("[INFO] This may take several minutes for large models (~3GB for large-v3)", flush=True)
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# Get file sizes to show progress
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try:
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fs = HfFileSystem()
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files_info = fs.ls(repo_id, detail=True)
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total_size = sum(f.get('size', 0) for f in files_info if f.get('name', '').split('/')[-1] in required_files)
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print(f"[INFO] Total download size: ~{format_bytes(total_size)}", flush=True)
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except Exception:
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pass # Size info is optional
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# Download with progress
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downloaded = 0
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start_time = time.time()
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for filename in required_files:
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file_start = time.time()
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print(f"[DOWNLOAD] {filename}...", end=" ", flush=True)
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try:
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local_path = hf_hub_download(
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repo_id=repo_id,
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filename=filename,
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resume_download=True,
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)
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elapsed = time.time() - file_start
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file_size = os.path.getsize(local_path) if os.path.exists(local_path) else 0
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print(f"done ({format_bytes(file_size)}, {elapsed:.1f}s)", flush=True)
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downloaded += 1
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# Return directory on first successful download
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if downloaded == 1:
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model_dir = os.path.dirname(local_path)
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except EntryNotFoundError:
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print("not found (optional)", flush=True)
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except Exception as e:
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print(f"error: {e}", flush=True)
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total_time = time.time() - start_time
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print(f"[INFO] Download complete in {total_time:.1f}s", flush=True)
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return model_dir
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except Exception as e:
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print(f"[WARN] Custom download failed ({e}), falling back to default", file=sys.stderr)
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return model_name
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def format_timestamp(seconds: float) -> str:
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td = timedelta(seconds=seconds)
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# Ensure SRT format HH:MM:SS,mmm
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@ -324,7 +439,21 @@ def main():
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compute_type = "float16" if device == "cuda" else "float32"
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print(f"[INFO] Loading model='{args.model}', device='{device}', compute_type='{compute_type}'")
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model = WhisperModel(args.model, device=device, compute_type=compute_type)
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# Pre-download model files with explicit progress if not already cached
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model_path = args.model
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if not os.path.isdir(args.model): # Not a local path, need to download from HF
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model_path = download_model_with_progress(args.model)
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# Show CTranslate2 conversion progress
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import logging
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logging.basicConfig(level=logging.INFO, format='[%(levelname)s] %(message)s')
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ct2_logger = logging.getLogger("faster_whisper")
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ct2_logger.setLevel(logging.INFO)
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print("[INFO] Initializing model...", flush=True)
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model = WhisperModel(model_path, device=device, compute_type=compute_type)
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print("[INFO] Model loaded successfully.", flush=True)
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# Transcription with live progress
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total_duration = get_media_duration(inp)
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194
scripts/misc/testsAndMisc-bash/tools/transcribe_helpers.py
Normal file
194
scripts/misc/testsAndMisc-bash/tools/transcribe_helpers.py
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@ -0,0 +1,194 @@
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#!/usr/bin/env python3
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"""Helper utilities for transcribe.sh - replaces inline Python snippets."""
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import argparse
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import math
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import os
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import sys
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import array
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import wave
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def get_python_version() -> str:
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"""Return Python major.minor version string."""
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return f"{sys.version_info.major}.{sys.version_info.minor}"
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def check_faster_whisper() -> bool:
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"""Check if faster_whisper is importable. Exit 7 if not."""
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try:
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import faster_whisper # noqa: F401
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return True
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except ImportError:
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return False
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def check_diarization_deps() -> bool:
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"""Check if diarization dependencies are available. Returns False with warning if missing."""
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try:
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import soundfile # noqa: F401
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import speechbrain # noqa: F401
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import torch # noqa: F401
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return True
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except Exception as e:
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print(f"[WARN] Diarization deps missing offline ({e}); speaker labels will be skipped.")
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return False
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def check_ctranslate2() -> bool:
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"""Check if ctranslate2 is importable."""
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try:
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import ctranslate2 # noqa: F401
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return True
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except ImportError:
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return False
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def print_deps_installed():
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"""Print confirmation that Python dependencies are installed."""
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print(f"[PY] Python {sys.version.split()[0]} dependencies installed.")
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def generate_sine_wav(outfile: str, frequency: float = 1000.0, duration: int = 3,
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sample_rate: int = 16000, amplitude: float = 0.3) -> bool:
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"""Generate a sine wave WAV file using only Python stdlib.
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Args:
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outfile: Output WAV file path
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frequency: Tone frequency in Hz (default: 1000)
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duration: Duration in seconds (default: 3)
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sample_rate: Sample rate in Hz (default: 16000)
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amplitude: Amplitude 0.0-1.0 (default: 0.3)
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Returns:
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True on success, False on failure
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"""
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try:
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n_samples = sample_rate * duration
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data = array.array("h", [
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int(max(-1.0, min(1.0, amplitude * math.sin(2 * math.pi * frequency * (i / sample_rate)))) * 32767)
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for i in range(n_samples)
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])
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with wave.open(outfile, "w") as wf:
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wf.setnchannels(1)
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wf.setsampwidth(2)
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wf.setframerate(sample_rate)
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wf.writeframes(data.tobytes())
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return True
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except Exception as e:
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print(f"[ERROR] Failed to generate WAV: {e}", file=sys.stderr)
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return False
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def prepare_model(model_name: str, model_dir: str) -> bool:
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"""Download a whisper model for offline use.
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Args:
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model_name: Model name (tiny, base, small, medium, large-v3, etc.)
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model_dir: Directory to store the model
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Returns:
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True on success, False on failure
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"""
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try:
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from faster_whisper import WhisperModel
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# Enable HuggingFace Hub progress bars for model download
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try:
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from huggingface_hub import logging as hf_logging
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hf_logging.set_verbosity_info()
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import huggingface_hub
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huggingface_hub.constants.HF_HUB_DISABLE_PROGRESS_BARS = False
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os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "0"
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except ImportError:
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pass
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print(f"[PY] Preparing model '{model_name}' into {model_dir}")
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print("[INFO] Downloading model files (progress bar should appear below)...", flush=True)
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WhisperModel(model_name, device="cpu", compute_type="int8", download_root=model_dir)
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print("[PY] Model prepared.")
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return True
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except Exception as e:
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print(f"[ERROR] Failed to prepare model: {e}", file=sys.stderr)
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return False
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def test_cuda() -> bool:
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"""Test CUDA initialization with faster-whisper.
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Returns:
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True if CUDA works, False otherwise
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"""
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try:
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from faster_whisper import WhisperModel
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WhisperModel("tiny", device="cuda", compute_type="float16")
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print("[PY] CUDA test init succeeded.")
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return True
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except Exception as e:
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print(f"[ERROR] CUDA test failed: {e}", file=sys.stderr)
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return False
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def main():
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parser = argparse.ArgumentParser(
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description="Helper utilities for transcribe.sh",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Commands:
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python-version Print Python major.minor version
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check-faster-whisper Check if faster_whisper is installed (exit 7 if not)
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check-diarization Check diarization deps (warn if missing)
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check-ctranslate2 Check if ctranslate2 is installed (exit 1 if not)
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deps-installed Print deps installed confirmation message
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generate-wav FILE Generate a 3s 1kHz sine wave WAV file
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prepare-model Download model for offline use (requires --model and --model-dir)
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test-cuda Test CUDA initialization
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""")
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parser.add_argument("command", choices=[
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"python-version",
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"check-faster-whisper",
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"check-diarization",
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"check-ctranslate2",
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"deps-installed",
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"generate-wav",
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"prepare-model",
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"test-cuda",
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], help="Command to run")
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parser.add_argument("--file", help="Output file path (for generate-wav)")
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parser.add_argument("--model", help="Model name (for prepare-model)")
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parser.add_argument("--model-dir", help="Model directory (for prepare-model)")
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args = parser.parse_args()
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if args.command == "python-version":
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print(get_python_version())
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elif args.command == "check-faster-whisper":
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if not check_faster_whisper():
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print("Python dependency 'faster_whisper' not found in offline mode. Run with --online to install.", file=sys.stderr)
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sys.exit(7)
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elif args.command == "check-diarization":
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check_diarization_deps()
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elif args.command == "check-ctranslate2":
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if not check_ctranslate2():
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sys.exit(1)
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elif args.command == "deps-installed":
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print_deps_installed()
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elif args.command == "generate-wav":
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if not args.file:
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print("--file is required for generate-wav", file=sys.stderr)
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sys.exit(2)
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if not generate_sine_wav(args.file):
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sys.exit(1)
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elif args.command == "prepare-model":
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if not args.model or not args.model_dir:
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print("--model and --model-dir are required for prepare-model", file=sys.stderr)
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sys.exit(2)
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if not prepare_model(args.model, args.model_dir):
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sys.exit(1)
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elif args.command == "test-cuda":
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if not test_cuda():
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sys.exit(1)
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if __name__ == "__main__":
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main()
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@ -11,6 +11,7 @@ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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PROJECT_DIR="$SCRIPT_DIR"
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TOOLS_DIR="$PROJECT_DIR/tools"
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PY_RUNNER="$TOOLS_DIR/transcribe_fw.py"
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PY_HELPERS="$TOOLS_DIR/transcribe_helpers.py"
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VENV_DIR="$PROJECT_DIR/.venv"
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usage() {
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@ -73,7 +74,7 @@ has_libcublas12() {
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# venv-provided NVIDIA CUDA libs
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if [[ -x "$VENV_DIR/bin/python" ]]; then
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local pyver
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pyver="$("$VENV_DIR"/bin/python -c 'import sys;print(f"{sys.version_info.major}.{sys.version_info.minor}")' 2>/dev/null || true)"
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pyver="$("$VENV_DIR"/bin/python "$PY_HELPERS" python-version 2>/dev/null || true)"
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if [[ -n $pyver ]]; then
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for d in "$VENV_DIR/lib/python$pyver/site-packages/nvidia/cublas/lib" \
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"$VENV_DIR/lib/python$pyver/site-packages/nvidia/cudnn/lib" \
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@ -234,46 +235,37 @@ install_python_deps() {
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export PIP_DEFAULT_TIMEOUT=${PIP_DEFAULT_TIMEOUT:-20}
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if [[ $OFFLINE -eq 1 ]]; then
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# Offline: do not install, just verify modules
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if ! python -c 'import faster_whisper' >/dev/null 2>&1; then
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echo "Python dependency 'faster_whisper' not found in offline mode. Run with --online to install." >&2
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if ! python "$PY_HELPERS" check-faster-whisper; then
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exit 7
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fi
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# If diarization requested offline, check for its deps too (warn-only)
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if [[ ${FW_DIARIZE:-} == "1" ]]; then
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python - <<'PY' || true
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try:
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import soundfile, speechbrain, torch # noqa: F401
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except Exception as e:
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print(f"[WARN] Diarization deps missing offline ({e}); speaker labels will be skipped.")
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PY
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python "$PY_HELPERS" check-diarization || true
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fi
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return 0
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fi
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if [[ $has_nvidia_flag -eq 1 ]]; then
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# If ctranslate2 is not installed, attempt CUDA-enabled wheel (quiet, with fallback)
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if ! "$VENV_DIR/bin/python" -c 'import ctranslate2' >/dev/null 2>&1; then
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# If ctranslate2 is not installed, attempt CUDA-enabled wheel (with fallback)
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if ! "$VENV_DIR/bin/python" "$PY_HELPERS" check-ctranslate2 2>/dev/null; then
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log "Installing CUDA-enabled CTranslate2 (cu12 wheel)"
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python -m pip install -q --retries 1 --upgrade "ctranslate2<5,>=4.0" --extra-index-url https://download.opennmt.net/ctranslate2/cu12 ||
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python -m pip install --progress-bar on --retries 1 --upgrade "ctranslate2<5,>=4.0" --extra-index-url https://download.opennmt.net/ctranslate2/cu12 ||
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log "Warning: could not reach cu12 wheel index; will proceed with available ctranslate2"
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fi
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# Ensure NVIDIA CUDA 12 runtime libs are available inside the venv
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python -m pip install -q --retries 1 --upgrade nvidia-cublas-cu12 nvidia-cuda-runtime-cu12 nvidia-cudnn-cu12 ||
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python -m pip install --progress-bar on --retries 1 --upgrade nvidia-cublas-cu12 nvidia-cuda-runtime-cu12 nvidia-cudnn-cu12 ||
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log "Warning: failed to install NVIDIA cu12 runtime libs via pip"
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fi
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python -m pip install -q --retries 1 --upgrade faster-whisper ffmpeg-python
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python -m pip install --progress-bar on --retries 1 --upgrade faster-whisper ffmpeg-python
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# If diarization requested and online, install its Python deps best-effort
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if [[ ${FW_DIARIZE:-} == "1" ]]; then
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python -m pip install -q --retries 1 --upgrade soundfile speechbrain ||
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python -m pip install --progress-bar on --retries 1 --upgrade soundfile speechbrain ||
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log "Warning: failed to install soundfile/speechbrain"
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# Torch and torchaudio CPU wheels (force to avoid mismatched CUDA builds)
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python -m pip install -q --retries 1 --upgrade --force-reinstall --index-url https://download.pytorch.org/whl/cpu torch torchaudio ||
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python -m pip install --progress-bar on --retries 1 --upgrade --force-reinstall --index-url https://download.pytorch.org/whl/cpu torch torchaudio ||
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log "Warning: failed to install torch/torchaudio CPU wheels"
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fi
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python - <<'PY'
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import sys
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print(f"[PY] Python {sys.version.split()[0]} dependencies installed.")
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PY
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python "$PY_HELPERS" deps-installed
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}
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ensure_runner() {
|
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@ -298,7 +290,7 @@ generate_test_audio() {
|
||||
# Fallback: generate tone via Python stdlib (no external deps)
|
||||
if [[ ! -s $tmpwav ]]; then
|
||||
log "Generating 3s 1kHz WAV via Python stdlib -> $tmpwav" >&2
|
||||
python3 -c 'import sys,wave,math,array;outfile=sys.argv[1];fr=16000;dur=3;freq=1000.0;ampl=0.3;n=fr*dur;data=array.array("h",[int(max(-1.0,min(1.0,ampl*math.sin(2*math.pi*freq*(i/fr))))*32767) for i in range(n)]);wf=wave.open(outfile,"w");wf.setnchannels(1);wf.setsampwidth(2);wf.setframerate(fr);wf.writeframes(data.tobytes());wf.close()' "$tmpwav" || true
|
||||
python3 "$PY_HELPERS" generate-wav --file "$tmpwav" || true
|
||||
fi
|
||||
# Final fallback: tone via ffmpeg
|
||||
if [[ ! -s $tmpwav ]]; then
|
||||
@ -315,15 +307,7 @@ prepare_model() {
|
||||
# shellcheck disable=SC1091
|
||||
source "$VENV_DIR/bin/activate"
|
||||
log "Preparing model '$name' into $MODEL_DIR"
|
||||
python - <<PY
|
||||
import sys, os
|
||||
from faster_whisper import WhisperModel
|
||||
name = os.environ.get('FW_PREPARE_NAME')
|
||||
root = os.environ.get('FW_MODEL_DIR')
|
||||
print(f"[PY] Preparing model '{name}' into {root}")
|
||||
WhisperModel(name, device="cpu", compute_type="int8", download_root=root)
|
||||
print("[PY] Model prepared.")
|
||||
PY
|
||||
python "$PY_HELPERS" prepare-model --model "$name" --model-dir "$MODEL_DIR"
|
||||
}
|
||||
|
||||
main() {
|
||||
@ -397,8 +381,6 @@ main() {
|
||||
exit 2
|
||||
fi
|
||||
install_python_deps 0
|
||||
export FW_PREPARE_NAME="$PREPARE_MODEL"
|
||||
export FW_MODEL_DIR="$MODEL_DIR"
|
||||
prepare_model "$PREPARE_MODEL"
|
||||
log "Model '$PREPARE_MODEL' downloaded to $MODEL_DIR"
|
||||
exit 0
|
||||
@ -445,7 +427,7 @@ main() {
|
||||
# Include system and possible venv-provided CUDA libs
|
||||
local pyver venv_cuda_paths=""
|
||||
if [[ -x "$VENV_DIR/bin/python" ]]; then
|
||||
pyver="$("$VENV_DIR"/bin/python -c 'import sys;print(f"{sys.version_info.major}.{sys.version_info.minor}")' 2>/dev/null || true)"
|
||||
pyver="$("$VENV_DIR"/bin/python "$PY_HELPERS" python-version 2>/dev/null || true)"
|
||||
if [[ -n $pyver ]]; then
|
||||
venv_cuda_paths="$VENV_DIR/lib/python$pyver/site-packages/nvidia/cublas/lib:$VENV_DIR/lib/python$pyver/site-packages/nvidia/cudnn/lib:$VENV_DIR/lib/python$pyver/site-packages/nvidia/cuda_runtime/lib"
|
||||
fi
|
||||
@ -454,7 +436,7 @@ main() {
|
||||
export PATH="${PATH}:${CUDA_HOME}/bin"
|
||||
# shellcheck disable=SC1091
|
||||
source "$VENV_DIR/bin/activate"
|
||||
python -c 'from faster_whisper import WhisperModel; WhisperModel("tiny", device="cuda", compute_type="float16"); print("[PY] CUDA test init succeeded.")' || {
|
||||
python "$PY_HELPERS" test-cuda || {
|
||||
echo "CUDA environment check failed. Aborting as requested." >&2
|
||||
exit 6
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user