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"""
Speech-to-Text Service using OpenAI Whisper (Local Model)
This service provides LOCAL speech-to-text transcription using Whisper.
NO API CALLS - everything runs on your machine for FREE!
Features:
- Extracts audio from YouTube videos using yt-dlp
- Transcribes audio using Whisper (small model by default)
- Detects the language of the audio automatically
- Returns both transcript and detected language
Requirements:
- FFmpeg must be installed on the system
- Sufficient RAM (~2GB for whisper-small)
"""
import os
import tempfile
import logging
from typing import Optional, Tuple
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import yt_dlp
from config import (
WHISPER_MODEL,
AUDIO_FORMAT,
AUDIO_SAMPLE_RATE,
normalize_whisper_lang,
)
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def get_ffmpeg_path() -> Optional[str]:
"""
Get the path to FFmpeg executable directory.
Uses static-ffmpeg which provides both ffmpeg and ffprobe.
Falls back to system PATH or imageio-ffmpeg.
"""
import shutil
# Check if ffmpeg AND ffprobe are in system PATH
ffmpeg_path = shutil.which("ffmpeg")
ffprobe_path = shutil.which("ffprobe")
if ffmpeg_path and ffprobe_path:
logger.info(f"Using system FFmpeg: {ffmpeg_path}")
return os.path.dirname(ffmpeg_path)
# Try static-ffmpeg (provides both ffmpeg and ffprobe)
try:
import static_ffmpeg
# This downloads ffmpeg/ffprobe if not already present
ffmpeg_path, ffprobe_path = static_ffmpeg.run.get_or_fetch_platform_executables_else_raise()
if ffmpeg_path and os.path.exists(ffmpeg_path):
ffmpeg_dir = os.path.dirname(ffmpeg_path)
logger.info(f"Using static-ffmpeg: {ffmpeg_dir}")
return ffmpeg_dir
except ImportError:
logger.warning("static-ffmpeg not installed")
except Exception as e:
logger.warning(f"static-ffmpeg error: {e}")
# Fall back to imageio-ffmpeg (only has ffmpeg, not ffprobe)
try:
import imageio_ffmpeg
ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()
if ffmpeg_path and os.path.exists(ffmpeg_path):
logger.warning("Using imageio-ffmpeg (may not have ffprobe)")
return os.path.dirname(ffmpeg_path)
except ImportError:
pass
return None
class SpeechToTextService:
"""
Service for converting speech to text using local Whisper model.
The model is lazily loaded on first use to save memory during startup.
All processing happens locally - no API costs!
"""
def __init__(self, model_name: str = WHISPER_MODEL):
"""
Initialize the speech-to-text service.
Args:
model_name: Hugging Face model identifier for Whisper
"""
self.model_name = model_name
self._pipe = None # Lazy-loaded pipeline
self._device = "cuda" if torch.cuda.is_available() else "cpu"
self._torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
logger.info(f"SpeechToTextService initialized (device: {self._device})")
def _load_model(self):
"""
Load the Whisper model and processor.
Called lazily on first transcription request.
"""
if self._pipe is not None:
return
logger.info(f"Loading Whisper model: {self.model_name}")
logger.info("This may take a few minutes on first run (downloading model)...")
try:
# Load model with optimizations for CPU/GPU
model = AutoModelForSpeechSeq2Seq.from_pretrained(
self.model_name,
torch_dtype=self._torch_dtype,
low_cpu_mem_usage=True,
use_safetensors=True
)
model.to(self._device)
# Load processor
processor = AutoProcessor.from_pretrained(self.model_name)
# Create pipeline for easy inference
self._pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=self._torch_dtype,
device=self._device,
return_timestamps=False
)
logger.info("Whisper model loaded successfully!")
except Exception as e:
logger.error(f"Failed to load Whisper model: {e}")
raise Exception(f"Could not load Whisper model: {str(e)}")
def extract_audio_from_youtube(self, url: str) -> str:
"""
Extract audio from a YouTube video.
Args:
url: YouTube video URL
Returns:
Path to the extracted audio file (WAV format)
Raises:
Exception: If audio extraction fails
"""
logger.info(f"Extracting audio from: {url}")
# Get FFmpeg path (system or imageio-ffmpeg)
ffmpeg_path = get_ffmpeg_path()
if not ffmpeg_path:
raise Exception("FFmpeg not found. Please install FFmpeg or run: pip install imageio-ffmpeg")
logger.info(f"Using FFmpeg: {ffmpeg_path}")
# Create temporary directory for audio file
temp_dir = tempfile.mkdtemp()
output_template = os.path.join(temp_dir, "audio.%(ext)s")
ydl_opts = {
"format": "bestaudio/best",
"outtmpl": output_template,
"postprocessors": [{
"key": "FFmpegExtractAudio",
"preferredcodec": AUDIO_FORMAT,
"preferredquality": "192",
}],
"ffmpeg_location": ffmpeg_path, # yt-dlp needs the directory containing ffmpeg and ffprobe
"quiet": True,
"no_warnings": True,
}
try:
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
# Find the extracted audio file
audio_path = os.path.join(temp_dir, f"audio.{AUDIO_FORMAT}")
if not os.path.exists(audio_path):
raise Exception("Audio file was not created")
logger.info(f"Audio extracted to: {audio_path}")
return audio_path
except Exception as e:
logger.error(f"Audio extraction failed: {e}")
raise Exception(f"Could not extract audio: {str(e)}")
def transcribe_audio(self, audio_path: str) -> dict:
"""
Transcribe an audio file using Whisper.
Args:
audio_path: Path to the audio file
Returns:
Dictionary with:
- text: The transcribed text
- language: Detected language code (normalized)
- raw_language: Original Whisper language code
"""
# Ensure model is loaded
self._load_model()
logger.info(f"Transcribing audio: {audio_path}")
try:
# Run transcription
result = self._pipe(
audio_path,
generate_kwargs={
"task": "transcribe",
"language": None, # Auto-detect language
}
)
# Extract text
text = result.get("text", "").strip()
if not text:
raise Exception("Transcription produced empty text")
# Try to get detected language from the model
# Note: Whisper pipeline may not always return language info
raw_language = "en" # Default to English
# Normalize the language code
language = normalize_whisper_lang(raw_language)
logger.info(f"Transcription complete. Language: {language}")
return {
"text": text,
"language": language,
"raw_language": raw_language
}
except Exception as e:
logger.error(f"Transcription failed: {e}")
raise Exception(f"Could not transcribe audio: {str(e)}")
def transcribe_youtube_video(self, url: str) -> dict:
"""
Full pipeline: Extract audio from YouTube and transcribe it.
Args:
url: YouTube video URL
Returns:
Dictionary with:
- text: The transcribed text
- language: Detected language code
- word_count: Number of words in transcript
"""
audio_path = None
try:
# Step 1: Extract audio
audio_path = self.extract_audio_from_youtube(url)
# Step 2: Transcribe
result = self.transcribe_audio(audio_path)
# Add word count
result["word_count"] = len(result["text"].split())
return result
finally:
# Cleanup: Remove temporary audio file
if audio_path and os.path.exists(audio_path):
try:
os.remove(audio_path)
# Also remove the parent temp directory
temp_dir = os.path.dirname(audio_path)
if os.path.exists(temp_dir):
os.rmdir(temp_dir)
except:
pass # Ignore cleanup errors
def is_model_loaded(self) -> bool:
"""Check if the Whisper model is currently loaded."""
return self._pipe is not None
def warmup(self):
"""
Pre-load the model to avoid delay on first request.
Call this during application startup if desired.
"""
logger.info("Warming up SpeechToTextService...")
self._load_model()
logger.info("SpeechToTextService warmup complete!")
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