Revamp: embedded console, faster-whisper, simplified install
This commit is contained in:
Kristofer Söderström
@@ -1,11 +1,56 @@
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import os
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import sys
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import datetime
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import site
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from glob import glob
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import whisper
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from torch import backends, cuda, Generator
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import colorama
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from colorama import Back,Fore
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colorama.init(autoreset=True)
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# ---------------------------------------------------------------------------
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# CUDA setup — must happen before importing faster_whisper / ctranslate2
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# ---------------------------------------------------------------------------
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def _setup_cuda_dlls():
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"""Add NVIDIA pip-package DLL dirs to the DLL search path (Windows only).
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pip-installed nvidia-cublas-cu12 / nvidia-cudnn-cu12 place their .dll
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files inside the site-packages tree. Python 3.8+ on Windows does NOT
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search PATH for DLLs loaded via ctypes/LoadLibrary, so we must
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explicitly register every nvidia/*/bin and nvidia/*/lib directory using
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os.add_dll_directory *and* prepend them to PATH (some native extensions
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still rely on PATH).
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"""
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if sys.platform != "win32":
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return
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try:
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for sp in site.getsitepackages():
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nvidia_root = os.path.join(sp, "nvidia")
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if not os.path.isdir(nvidia_root):
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continue
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for pkg in os.listdir(nvidia_root):
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for sub in ("bin", "lib"):
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d = os.path.join(nvidia_root, pkg, sub)
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if os.path.isdir(d):
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os.environ["PATH"] = d + os.pathsep + os.environ.get("PATH", "")
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try:
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os.add_dll_directory(d)
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except (OSError, AttributeError):
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pass
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except Exception:
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pass
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_setup_cuda_dlls()
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from faster_whisper import WhisperModel
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def _detect_device():
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"""Return (device, compute_type) for the best available backend."""
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try:
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import ctranslate2
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cuda_types = ctranslate2.get_supported_compute_types("cuda")
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if "float16" in cuda_types:
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return "cuda", "float16"
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except Exception:
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pass
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return "cpu", "int8"
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# Get the path
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@@ -16,12 +61,12 @@ def get_path(path):
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# Main function
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def transcribe(path, glob_file, model=None, language=None, verbose=False):
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"""
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Transcribes audio files in a specified folder using OpenAI's Whisper model.
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Transcribes audio files in a specified folder using faster-whisper (CTranslate2).
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Args:
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path (str): Path to the folder containing the audio files.
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glob_file (list): List of audio file paths to transcribe.
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model (str, optional): Name of the Whisper model to use for transcription.
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model (str, optional): Name of the Whisper model size to use for transcription.
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Defaults to None, which uses the default model.
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language (str, optional): Language code for transcription. Defaults to None,
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which enables automatic language detection.
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@@ -38,59 +83,67 @@ def transcribe(path, glob_file, model=None, language=None, verbose=False):
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- The function downloads the specified model if not available locally.
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- The transcribed text files will be saved in a "transcriptions" folder
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within the specified path.
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- Uses CTranslate2 for up to 4x faster inference compared to openai-whisper.
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- FFmpeg is bundled via the PyAV dependency — no separate installation needed.
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"""
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# Check for GPU acceleration and set device
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if backends.mps.is_available():
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device = 'mps'
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Generator('mps').manual_seed(42)
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elif cuda.is_available():
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device = 'cuda'
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Generator('cuda').manual_seed(42)
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else:
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device = 'cpu'
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Generator().manual_seed(42)
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SEP = "─" * 46
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# Load model on the correct device
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model = whisper.load_model(model, device=device)
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# Start main loop
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files_transcripted=[]
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# ── Step 1: Detect hardware ──────────────────────────────────────
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device, compute_type = _detect_device()
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print(f"⚙ Device: {device} | Compute: {compute_type}")
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# ── Step 2: Load model ───────────────────────────────────────────
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print(f"⏳ Loading model '{model}' — downloading if needed...")
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whisper_model = WhisperModel(model, device=device, compute_type=compute_type)
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print("✅ Model ready!")
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print(SEP)
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# ── Step 3: Transcribe files ─────────────────────────────────────
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total_files = len(glob_file)
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print(f"📂 Found {total_files} item(s) in folder")
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print(SEP)
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files_transcripted = []
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file_num = 0
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for file in glob_file:
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title = os.path.basename(file).split('.')[0]
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print(Back.CYAN + '\nTrying to transcribe file named: {}\U0001f550'.format(title))
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file_num += 1
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print(f"\n{'─' * 46}")
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print(f"📄 File {file_num}/{total_files}: {title}")
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try:
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result = model.transcribe(
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file,
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language=language,
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verbose=verbose
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)
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files_transcripted.append(result)
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# Make folder if missing
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try:
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os.makedirs('{}/transcriptions'.format(path), exist_ok=True)
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except FileExistsError:
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pass
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# Create segments for text files
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start = []
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end = []
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text = []
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for segment in result['segments']:
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start.append(str(datetime.timedelta(seconds=segment['start'])))
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end.append(str(datetime.timedelta(seconds=segment['end'])))
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text.append(segment['text'])
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# Save files to transcriptions folder
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with open("{}/transcriptions/{}.txt".format(path, title), 'w', encoding='utf-8') as file:
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file.write(title)
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for i in range(len(result['segments'])):
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file.write('\n[{} --> {}]:{}'.format(start[i], end[i], text[i]))
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# Skip invalid files
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except RuntimeError:
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print(Fore.RED + 'Not a valid file, skipping.')
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pass
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# Check if any files were processed.
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segments, info = whisper_model.transcribe(
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file,
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language=language,
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beam_size=5
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)
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# Make folder if missing
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os.makedirs('{}/transcriptions'.format(path), exist_ok=True)
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# Stream segments as they are decoded
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segment_list = []
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with open("{}/transcriptions/{}.txt".format(path, title), 'w', encoding='utf-8') as f:
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f.write(title)
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for seg in segments:
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start_ts = str(datetime.timedelta(seconds=seg.start))
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end_ts = str(datetime.timedelta(seconds=seg.end))
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f.write('\n[{} --> {}]:{}'.format(start_ts, end_ts, seg.text))
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f.flush()
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if verbose:
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print(" [%.2fs → %.2fs] %s" % (seg.start, seg.end, seg.text))
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else:
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print(" Transcribed up to %.0fs..." % seg.end, end='\r')
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segment_list.append(seg)
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print(f"✅ Done — saved to transcriptions/{title}.txt")
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files_transcripted.append(segment_list)
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except Exception:
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print('⚠ Not a valid audio/video file, skipping.')
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# ── Summary ──────────────────────────────────────────────────────
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print(f"\n{SEP}")
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if len(files_transcripted) > 0:
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output_text = 'Finished transcription, {} files can be found in {}/transcriptions'.format(len(files_transcripted), path)
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output_text = f"✅ Finished! {len(files_transcripted)} file(s) transcribed.\n Saved in: {path}/transcriptions"
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else:
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output_text = 'No files elligible for transcription, try adding audio or video files to this folder or choose another folder!'
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# Return output text
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output_text = '⚠ No files eligible for transcription — try another folder.'
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print(output_text)
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print(SEP)
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return output_text
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