Whisper Transcription Expert Skill

Overview

Expert-level knowledge for production Whisper transcription, specializing in Thai language optimization, GPU efficiency, and high-accuracy audio-to-text conversion for video localization workflows.

New: Includes Faster-Whisper integration (4-5x speed boost, 62% less RAM, same accuracy)

When to use this skill:

• Transcribing Thai audio/video to SRT
• Optimizing Whisper accuracy for specific languages
• Using Faster-Whisper for 4-5x speed boost ⚡
• Implementing VAD (Voice Activity Detection)
• Managing long audio files with smart chunking
• Removing Whisper hallucinations
• GPU memory optimization
• Batch processing multiple videos (tmux/background)
• Production transcription pipelines

---

Table of Contents

1. Model Selection
2. Thai Language Optimization
3. Smart Chunking Strategies
4. VAD Integration
5. Hallucination Removal
6. GPU Optimization
7. SRT Generation
8. Production Best Practices
9. Platform-Specific Guides
10. Common Pitfalls & Solutions

---

Model Selection

Available Whisper Models

Standard OpenAI Whisper:

Model	Size	Parameters	Accuracy	Speed	VRAM	Best For
tiny	39 MB	39M	⭐	⚡⚡⚡	1 GB	Testing only
base	74 MB	74M	⭐⭐	⚡⚡⚡	1 GB	Quick drafts
small	244 MB	244M	⭐⭐⭐	⚡⚡	2 GB	General use
medium	769 MB	769M	⭐⭐⭐⭐	⚡	5 GB	Quality work
large-v2	1.5 GB	1550M	⭐⭐⭐⭐⭐	🐌	10 GB	Production
large-v3	1.5 GB	1550M	⭐⭐⭐⭐⭐+	🐌	10 GB	Production (Latest)

⚡ Faster-Whisper (Recommended for Production):

Model	Size	Parameters	Accuracy	Speed	VRAM	Best For
large-v3 (INT8)	1.5 GB	1550M	⭐⭐⭐⭐⭐+	⚡⚡⚡⚡	4 GB	Production (4-5x faster!)
large-v3 (FP16)	1.5 GB	1550M	⭐⭐⭐⭐⭐+	⚡⚡⚡	6 GB	High accuracy + speed

⚡ Whisper vs Faster-Whisper Comparison

Feature	OpenAI Whisper	Faster-Whisper	Winner
Speed	1x (baseline)	4-5x faster ⚡	Faster-Whisper 🏆
RAM Usage	10 GB	4 GB (62% less)	Faster-Whisper 🏆
Accuracy	95%+	95%+ (same)	Tie ✅
Installation	pip install openai-whisper	pip install faster-whisper	Equal
GPU Support	PyTorch CUDA	CTranslate2 CUDA	Equal
API	whisper.load_model()	WhisperModel()	Different

Performance Metrics (10-minute Thai video):

Engine	Transcription Time	RAM Usage	GPU Utilization
OpenAI Whisper	~10 minutes	~10 GB	85-95%
Faster-Whisper	~2-3 minutes ⚡	~4 GB	90-100%

Why Faster-Whisper is Better

Technical Advantages:

1. INT8 Quantization

   • Reduces precision from float32 → int8 (32 bits → 8 bits)
   • 4x smaller memory footprint
   • Only 0.1% accuracy loss (negligible for Thai)
   • Enables larger batch processing

2. CTranslate2 Engine

   • Inference-optimized (vs PyTorch general-purpose)
   • Hardware-aware optimizations (GPU Tensor Cores, CPU AVX2/AVX-512)
   • Better memory management (streaming model loading)
   • Custom CUDA kernels for speed

3. Better Batching

   • Parallel segment processing (vs sequential)
   • Dynamic batch size optimization
   • Reduced GPU idle time

4. Memory Efficiency

   • Streaming model loading (not all-at-once)
   • Automatic cache management
   • Lower VRAM requirements → bigger batch sizes

When to Use Each:

Use Case	Recommended Engine	Reason
Production transcription	⚡ Faster-Whisper	4-5x speed, same accuracy
Long videos (>30 min)	⚡ Faster-Whisper	Lower RAM, faster processing
Batch processing	⚡ Faster-Whisper	Process 4-5x more files/hour
Development/testing	OpenAI Whisper	Simpler API, more familiar
Research	OpenAI Whisper	Direct PyTorch access

Language-Specific Recommendations

Thai Language:

• ✅ large-v3 - Best accuracy (95%+)
• ⚠️ medium - Acceptable (85-90%)
• ❌ small/base - Poor (<80%)

English:

• ✅ large-v3 or medium - Both excellent (98%+)
• ✅ small - Good enough for drafts (92%+)

Multilingual (Code-switching):

• ✅ large-v3 only - Required for accurate language detection

Installation

Option 1: OpenAI Whisper (Standard)

# Install Whisper
pip install -U openai-whisper

# Install dependencies
pip install ffmpeg-python

# For GPU support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Option 2: Faster-Whisper (Recommended for Production) ⚡

# Install Faster-Whisper
pip install faster-whisper

# Install dependencies (if not already installed)
pip install ffmpeg-python

# GPU support is included automatically
# No need to install separate PyTorch

Note: You can install both! They don't conflict with each other.

# Install both for flexibility
pip install openai-whisper faster-whisper

Basic Usage

OpenAI Whisper (Standard):

import whisper

# Load model (once)
model = whisper.load_model("large-v3")

# Transcribe
result = model.transcribe("audio.mp3")

print(result["text"])  # Full transcription
print(result["segments"])  # Timestamped segments

Faster-Whisper (4-5x faster!) ⚡:

from faster_whisper import WhisperModel

# Load model (once)
# device="cuda" for GPU, "cpu" for CPU
# compute_type="int8" for speed, "float16" for quality
model = WhisperModel("large-v3", device="cuda", compute_type="int8")

# Transcribe (returns generator!)
segments, info = model.transcribe("audio.mp3", language="th")

# Convert to list (if needed)
segments_list = list(segments)

# Print results
for segment in segments_list:
    print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")

# Or convert to OpenAI Whisper format
result = {
    "text": "",
    "segments": [],
    "language": info.language
}

for segment in segments_list:
    result["text"] += segment.text + " "
    result["segments"].append({
        "start": segment.start,
        "end": segment.end,
        "text": segment.text
    })

Faster-Whisper with Word Timestamps:

from faster_whisper import WhisperModel

model = WhisperModel("large-v3", device="cuda", compute_type="int8")

# Enable word timestamps
segments, info = model.transcribe(
    "audio.mp3",
    language="th",
    word_timestamps=True  # Enable word-level timestamps
)

for segment in segments:
    print(f"Segment: {segment.text}")

    # Word-level timestamps
    if segment.words:
        for word in segment.words:
            print(f"  [{word.start:.2f}s -> {word.end:.2f}s] {word.word}")

---

Thai Language Optimization

Critical: Initial Prompt

The single most important factor for Thai accuracy is the initial prompt:

# ✅ CORRECT - Dramatically improves Thai accuracy
result = model.transcribe(
    audio_path,
    language="th",  # Explicitly set Thai
    initial_prompt="นี่คือการบรรยายเกี่ยวกับ Forex การเทรด และการลงทุน ผู้บรรยายพูดภาษาไทยชัดเจน"
)

# ❌ WRONG - Auto-detect often switches to English mid-sentence
result = model.transcribe(audio_path)

Why Initial Prompt Matters:

• Provides context for domain vocabulary
• Primes the model for Thai language
• Reduces hallucinations
• Prevents language switching
• Improves accuracy by 5-10%

Domain-Specific Initial Prompts

Financial/Forex Content:

initial_prompt = """
นี่คือการบรรยายเกี่ยวกับ Forex การเทรด การลงทุน
คำศัพท์: ราคา กราฟ แนวต้าน แนวรับ เทรนด์ สัญญาณ โบรกเกอร์
ผู้บรรยายพูดภาษาไทยชัดเจน
"""

Technical/Programming Content:

initial_prompt = """
การสอนเทคโนโลยี โปรแกรมมิ่ง การพัฒนาซอฟต์แวร์
คำศัพท์: โค้ด ฟังก์ชัน ตัวแปร อัลกอริทึม
ผู้บรรยายพูดภาษาไทยชัดเจน
"""

General/Educational Content:

initial_prompt = """
การบรรยายภาษาไทยทั่วไป เนื้อหาการศึกษา
ผู้บรรยายพูดภาษาไทยชัดเจน มีคำศัพท์ทั่วไป
"""

Full Thai Transcription Template

import whisper
import torch

def transcribe_thai_audio(
    audio_path: str,
    model_name: str = "large-v3",
    initial_prompt: str = None,
    device: str = None
) -> dict:
    """
    Optimized Thai transcription with Whisper

    Args:
        audio_path: Path to audio file
        model_name: Whisper model (default: large-v3)
        initial_prompt: Domain-specific context
        device: 'cuda' or 'cpu' (auto-detect if None)

    Returns:
        dict with 'text', 'segments', 'language'
    """
    # Auto-detect device
    if device is None:
        device = "cuda" if torch.cuda.is_available() else "cpu"

    # Default Thai prompt
    if initial_prompt is None:
        initial_prompt = "นี่คือการบรรยายภาษาไทย ผู้บรรยายพูดชัดเจน"

    # Load model
    print(f"Loading {model_name} on {device}...")
    model = whisper.load_model(model_name, device=device)

    # Transcribe with Thai optimization
    print(f"Transcribing {audio_path}...")
    result = model.transcribe(
        audio_path,
        language="th",              # Force Thai
        initial_prompt=initial_prompt,
        word_timestamps=True,       # Enable word-level timing
        fp16=(device == "cuda"),    # Use FP16 on GPU for 2x speed
        temperature=0.0,            # Deterministic output
        beam_size=5,                # Beam search for accuracy
        best_of=5,                  # Generate 5 candidates
        condition_on_previous_text=True  # Use context from previous segments
    )

    print(f"Transcription complete: {len(result['segments'])} segments")
    return result

# Usage
result = transcribe_thai_audio(
    "thai_forex_lesson.mp3",
    initial_prompt="นี่คือการบรรยายเกี่ยวกับ Forex การเทรด และการลงทุน"
)

print(result["text"])

---

Smart Chunking Strategies

Problem: Whisper Has Maximum Length

Whisper optimal length: ~30 seconds per chunk Problem: Cutting mid-word destroys context

Solution 1: Silence-Based Chunking

from pydub import AudioSegment
from pydub.silence import detect_nonsilent
from pathlib import Path

def smart_chunk_audio(
    audio_path: str,
    max_duration_ms: int = 30000,
    min_silence_len: int = 500,
    silence_thresh: int = -40,
    output_dir: str = "chunks"
) -> list:
    """
    Split audio at silence points (never mid-word!)

    Args:
        audio_path: Input audio file
        max_duration_ms: Max chunk duration (30s default)
        min_silence_len: Minimum silence to consider (500ms)
        silence_thresh: Silence threshold in dB (-40 default)
        output_dir: Where to save chunks

    Returns:
        List of chunk file paths
    """
    # Load audio
    audio = AudioSegment.from_file(audio_path)
    print(f"Audio duration: {len(audio) / 1000:.2f}s")

    # Detect voice activity (non-silent ranges)
    nonsilent_ranges = detect_nonsilent(
        audio,
        min_silence_len=min_silence_len,
        silence_thresh=silence_thresh
    )

    print(f"Found {len(nonsilent_ranges)} voice segments")

    # Group into chunks at silence points
    chunks = []
    current_chunk_start = 0
    current_duration = 0

    for i, (start, end) in enumerate(nonsilent_ranges):
        segment_duration = end - start

        # Check if adding this segment exceeds max duration
        if current_duration + segment_duration > max_duration_ms:
            # Save current chunk (ends at silence before this segment)
            chunks.append({
                'start': current_chunk_start,
                'end': start,
                'duration': start - current_chunk_start
            })

            # Start new chunk
            current_chunk_start = start
            current_duration = segment_duration
        else:
            current_duration += segment_duration

    # Add final chunk
    if current_duration > 0:
        chunks.append({
            'start': current_chunk_start,
            'end': len(audio),
            'duration': len(audio) - current_chunk_start
        })

    # Export chunks
    Path(output_dir).mkdir(exist_ok=True)
    chunk_files = []

    for i, chunk in enumerate(chunks):
        chunk_audio = audio[chunk['start']:chunk['end']]
        chunk_file = f"{output_dir}/chunk_{i:04d}.wav"
        chunk_audio.export(chunk_file, format="wav")
        chunk_files.append(chunk_file)
        print(f"Chunk {i}: {chunk['duration']/1000:.2f}s → {chunk_file}")

    return chunk_files

# Usage
chunks = smart_chunk_audio("long_audio.mp3", max_duration_ms=25000)
print(f"Created {len(chunks)} chunks")

Solution 2: Overlapping Chunks (Better Context)

def create_overlapping_chunks(
    audio_path: str,
    chunk_duration_ms: int = 25000,
    overlap_ms: int = 2000,
    output_dir: str = "chunks"
) -> list:
    """
    Create overlapping chunks for better context preservation

    Args:
        audio_path: Input audio
        chunk_duration_ms: Chunk size (25s)
        overlap_ms: Overlap between chunks (2s)
        output_dir: Output directory

    Returns:
        List of (chunk_file, start_ms, end_ms)
    """
    audio = AudioSegment.from_file(audio_path)
    total_duration = len(audio)

    chunks = []
    start = 0
    chunk_num = 0

    Path(output_dir).mkdir(exist_ok=True)

    while start < total_duration:
        # Calculate chunk boundaries
        end = min(start + chunk_duration_ms, total_duration)

        # Extract chunk
        chunk_audio = audio[start:end]

        # Export
        chunk_file = f"{output_dir}/chunk_{chunk_num:04d}.wav"
        chunk_audio.export(chunk_file, format="wav")

        chunks.append({
            'file': chunk_file,
            'start_ms': start,
            'end_ms': end,
            'duration_ms': end - start
        })

        print(f"Chunk {chunk_num}: {start/1000:.2f}s - {end/1000:.2f}s")

        # Move start by (chunk_duration - overlap)
        start += (chunk_duration_ms - overlap_ms)
        chunk_num += 1

    return chunks

# Usage
chunks = create_overlapping_chunks(
    "long_audio.mp3",
    chunk_duration_ms=25000,
    overlap_ms=2000
)

---

VAD Integration

Why Use VAD (Voice Activity Detection)?

Benefits:

• ⚡ 2-3x faster (skip silence)
• 💰 Save GPU/API costs
• 📊 Better timestamps (no silence gaps)
• 🎯 Reduce hallucinations (Whisper hallucinates on silence)

WebRTC VAD Implementation

import webrtcvad
import wave
import contextlib
from pathlib import Path

def remove_silence_vad(
    input_path: str,
    output_path: str,
    aggressiveness: int = 2,
    sample_rate: int = 16000
) -> dict:
    """
    Remove silence using WebRTC VAD

    Args:
        input_path: Input audio file
        output_path: Output audio file (voice only)
        aggressiveness: VAD aggressiveness (0-3, higher = more aggressive)
        sample_rate: Must be 8000, 16000, 32000, or 48000 Hz

    Returns:
        dict with statistics
    """
    from pydub import AudioSegment

    # Load and convert to proper format for VAD
    audio = AudioSegment.from_file(input_path)
    audio = audio.set_frame_rate(sample_rate).set_channels(1)

    # Initialize VAD
    vad = webrtcvad.Vad(aggressiveness)

    # Process in 30ms frames (VAD requirement)
    frame_duration_ms = 30
    frame_length = int(sample_rate * frame_duration_ms / 1000) * 2  # 2 bytes per sample

    # Collect voice frames
    voice_frames = []
    total_frames = 0
    voice_frames_count = 0

    # Convert to raw audio
    raw_audio = audio.raw_data

    for i in range(0, len(raw_audio), frame_length):
        frame = raw_audio[i:i+frame_length]

        # Pad last frame if needed
        if len(frame) < frame_length:
            frame = frame + b'\x00' * (frame_length - len(frame))

        total_frames += 1

        # Check if frame contains speech
        try:
            is_speech = vad.is_speech(frame, sample_rate)
            if is_speech:
                voice_frames.append(frame)
                voice_frames_count += 1
        except:
            # If VAD fails, keep the frame (safer)
            voice_frames.append(frame)
            voice_frames_count += 1

    # Combine voice-only frames
    voice_audio = b''.join(voice_frames)

    # Convert back to AudioSegment
    result = AudioSegment(
        data=voice_audio,
        sample_width=audio.sample_width,
        frame_rate=sample_rate,
        channels=1
    )

    # Export
    result.export(output_path, format="wav")

    # Statistics
    original_duration = len(audio) / 1000  # seconds
    voice_duration = len(result) / 1000
    silence_removed = original_duration - voice_duration
    reduction_pct = (silence_removed / original_duration) * 100

    stats = {
        'original_duration_s': original_duration,
        'voice_duration_s': voice_duration,
        'silence_removed_s': silence_removed,
        'reduction_pct': reduction_pct,
        'total_frames': total_frames,
        'voice_frames': voice_frames_count
    }

    print(f"VAD Results:")
    print(f"  Original: {original_duration:.2f}s")
    print(f"  Voice only: {voice_duration:.2f}s")
    print(f"  Silence removed: {silence_removed:.2f}s ({reduction_pct:.1f}%)")

    return stats

# Usage
stats = remove_silence_vad(
    "input.mp3",
    "voice_only.wav",
    aggressiveness=2  # 0=least aggressive, 3=most aggressive
)

VAD + Whisper Pipeline

def transcribe_with_vad(
    audio_path: str,
    model_name: str = "large-v3",
    vad_aggressiveness: int = 2,
    initial_prompt: str = None
) -> dict:
    """
    Complete pipeline: VAD → Whisper transcription
    """
    import tempfile
    import os

    # Step 1: Remove silence with VAD
    print("Step 1: Removing silence with VAD...")
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
        vad_output = tmp.name

    vad_stats = remove_silence_vad(
        audio_path,
        vad_output,
        aggressiveness=vad_aggressiveness
    )

    # Step 2: Transcribe voice-only audio
    print("\nStep 2: Transcribing voice-only audio...")
    result = transcribe_thai_audio(
        vad_output,
        model_name=model_name,
        initial_prompt=initial_prompt
    )

    # Cleanup
    os.unlink(vad_output)

    # Add VAD stats to result
    result['vad_stats'] = vad_stats

    return result

# Usage
result = transcribe_with_vad(
    "thai_lesson.mp3",
    model_name="large-v3",
    vad_aggressiveness=2,
    initial_prompt="นี่คือการบรรยายภาษาไทย"
)

print(f"Transcription: {result['text']}")
print(f"Time saved: {result['vad_stats']['reduction_pct']:.1f}%")

---

Hallucination Removal

Common Whisper Hallucinations

Problem: Whisper hallucinates when given silence or noise

Common hallucinations:

• "ขอบคุณครับ ขอบคุณครับ ขอบคุณครับ..." (repeated thanks)
• "Thank you for watching" (default English phrase)
• "[Music]", "[Applause]", "[Laughter]"
• "www.example.com" (fake URLs)
• "Subscribe and like" (YouTube-like phrases)

Hallucination Detection & Removal

import re
from collections import Counter

def clean_whisper_transcript(
    text: str,
    remove_repetitions: bool = True,
    remove_markers: bool = True,
    remove_urls: bool = True,
    repetition_threshold: int = 3
) -> str:
    """
    Remove Whisper hallucinations and clean transcript

    Args:
        text: Raw Whisper output
        remove_repetitions: Remove repeated phrases
        remove_markers: Remove [Music], (laughs), etc.
        remove_urls: Remove URLs
        repetition_threshold: How many times = hallucination (default: 3)

    Returns:
        Cleaned text
    """
    original_text = text

    # 1. Remove markers: [Music], [Applause], (coughs), ♪ notes ♪
    if remove_markers:
        patterns = [
            r'\[.*?\]',          # [Music], [Applause]
            r'\(.*?\)',          # (laughs), (coughs)
            r'♪.*?♪',            # ♪ music ♪
            r'\{.*?\}',          # {sound effect}
        ]
        for pattern in patterns:
            text = re.sub(pattern, '', text)

    # 2. Remove URLs
    if remove_urls:
        text = re.sub(r'https?://\S+', '', text)
        text = re.sub(r'www\.\S+\.\S+', '', text)

    # 3. Remove repeated phrases (hallucination detector)
    if remove_repetitions:
        # Find phrases that repeat more than threshold times
        words = text.split()

        # Check 2-5 word phrases
        for phrase_len in range(2, 6):
            phrases = [' '.join(words[i:i+phrase_len])
                      for i in range(len(words) - phrase_len + 1)]
            phrase_counts = Counter(phrases)

            for phrase, count in phrase_counts.items():
                if count >= repetition_threshold:
                    # This phrase repeats too much - likely hallucination
                    text = text.replace(phrase, '', count - 1)  # Keep one copy
                    print(f"Removed repeated phrase: '{phrase}' ({count} times)")

    # 4. Fix spacing
    text = re.sub(r'\s+', ' ', text)  # Multiple spaces → single space
    text = text.strip()

    # 5. Fix Thai punctuation spacing
    text = re.sub(r'\s+([.,!?])', r'\1', text)  # Remove space before punctuation
    text = re.sub(r'([.,!?])(?=[^\s])', r'\1 ', text)  # Add space after punctuation

    # 6. Remove common Thai hallucinations
    thai_hallucinations = [
        r'(ขอบคุณครับ\s*){3,}',    # Repeated "thank you"
        r'(สวัสดีครับ\s*){3,}',     # Repeated "hello"
        r'(จบแล้วครับ\s*){3,}',     # Repeated "finished"
    ]
    for pattern in thai_hallucinations:
        text = re.sub(pattern, '', text)

    # Show what was removed
    chars_removed = len(original_text) - len(text)
    if chars_removed > 0:
        print(f"Cleaned transcript: removed {chars_removed} characters")

    return text

# Usage
raw_text = """
นี่คือการบรรยายเกี่ยวกับ Forex [Music] การเทรดนั้นสำคัญมาก
ขอบคุณครับ ขอบคุณครับ ขอบคุณครับ ขอบคุณครับ
เราต้องดูกราฟให้ดี www.example.com
"""

cleaned = clean_whisper_transcript(raw_text)
print(cleaned)
# Output: "นี่คือการบรรยายเกี่ยวกับ Forex การเทรดนั้นสำคัญมาก เราต้องดูกราฟให้ดี"

Hallucination Prevention (Better than Removal!)

def transcribe_anti_hallucination(
    audio_path: str,
    model_name: str = "large-v3",
    initial_prompt: str = None,
    no_speech_threshold: float = 0.6,
    logprob_threshold: float = -1.0
) -> dict:
    """
    Transcribe with hallucination prevention

    Args:
        audio_path: Input audio
        model_name: Whisper model
        initial_prompt: Context prompt
        no_speech_threshold: Higher = more aggressive silence detection (0-1)
        logprob_threshold: Higher = reject low-confidence outputs

    Returns:
        Transcription result
    """
    model = whisper.load_model(model_name)

    result = model.transcribe(
        audio_path,
        language="th",
        initial_prompt=initial_prompt,
        temperature=0.0,  # Deterministic (less creative hallucinations)
        no_speech_threshold=no_speech_threshold,  # Detect silence better
        logprob_threshold=logprob_threshold,  # Reject low-confidence
        compression_ratio_threshold=2.4,  # Reject overly compressed text
        condition_on_previous_text=True,  # Use context
        fp16=True
    )

    return result

---

GPU Optimization

Check GPU Availability

import torch

def check_gpu():
    """
    Check GPU availability and specs
    """
    if torch.cuda.is_available():
        gpu_name = torch.cuda.get_device_name(0)
        gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9
        print(f"✅ GPU Available: {gpu_name}")
        print(f"   Memory: {gpu_memory:.2f} GB")
        return "cuda"
    else:
        print("❌ No GPU detected - using CPU (will be slow!)")
        return "cpu"

device = check_gpu()

GPU Memory Management

import gc

def transcribe_with_memory_management(
    audio_files: list,
    model_name: str = "large-v3",
    batch_size: int = 1
) -> list:
    """
    Transcribe multiple files with GPU memory cleanup

    Args:
        audio_files: List of audio file paths
        model_name: Whisper model
        batch_size: Files to process before cleanup

    Returns:
        List of transcription results
    """
    device = check_gpu()

    # Load model once
    print(f"Loading {model_name}...")
    model = whisper.load_model(model_name, device=device)

    results = []

    for i, audio_file in enumerate(audio_files):
        print(f"\nProcessing {i+1}/{len(audio_files)}: {audio_file}")

        # Transcribe
        result = model.transcribe(
            audio_file,
            language="th",
            fp16=(device == "cuda"),  # Use FP16 on GPU for 2x speed
            temperature=0.0
        )

        results.append(result)

        # Clean up GPU memory every batch_size files
        if (i + 1) % batch_size == 0 and device == "cuda":
            print("Cleaning GPU memory...")
            torch.cuda.empty_cache()
            gc.collect()

    # Final cleanup
    if device == "cuda":
        torch.cuda.empty_cache()
        gc.collect()

    return results

# Usage
files = ["file1.mp3", "file2.mp3", "file3.mp3"]
results = transcribe_with_memory_management(files, batch_size=1)

FP16 Optimization (2x Faster on GPU)

# ✅ ALWAYS use fp16=True on GPU
result = model.transcribe(
    audio_path,
    fp16=True,  # ← 2x faster with minimal accuracy loss
    language="th"
)

# ❌ DON'T use fp16=True on CPU (will fail)
device = "cuda" if torch.cuda.is_available() else "cpu"
result = model.transcribe(
    audio_path,
    fp16=(device == "cuda"),  # Conditional
    language="th"
)

---

SRT Generation

Convert Whisper Segments → SRT

def whisper_to_srt(
    segments: list,
    output_path: str,
    max_chars_per_line: int = 42,
    max_duration_s: float = 7.0
) -> None:
    """
    Generate SRT file from Whisper segments

    Args:
        segments: Whisper result['segments']
        output_path: Output SRT file path
        max_chars_per_line: Max characters per subtitle line
        max_duration_s: Max subtitle duration (seconds)
    """
    with open(output_path, 'w', encoding='utf-8') as f:
        for i, segment in enumerate(segments, start=1):
            # SRT index
            f.write(f"{i}\n")

            # Timestamps
            start = format_srt_timestamp(segment['start'])
            end = format_srt_timestamp(segment['end'])
            f.write(f"{start} --> {end}\n")

            # Text (cleaned)
            text = clean_whisper_transcript(segment['text'])

            # Split long text into multiple lines
            lines = split_subtitle_text(text, max_chars_per_line)
            f.write('\n'.join(lines) + '\n\n')

    print(f"SRT saved: {output_path} ({len(segments)} segments)")

def format_srt_timestamp(seconds: float) -> str:
    """
    Convert seconds → SRT timestamp (HH:MM:SS,mmm)

    Args:
        seconds: Time in seconds (float)

    Returns:
        Formatted timestamp string
    """
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    millis = int((seconds % 1) * 1000)

    return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"

def split_subtitle_text(text: str, max_chars: int = 42) -> list:
    """
    Split long subtitle text into multiple lines

    Args:
        text: Subtitle text
        max_chars: Max characters per line

    Returns:
        List of lines
    """
    words = text.split()
    lines = []
    current_line = ""

    for word in words:
        test_line = current_line + " " + word if current_line else word

        if len(test_line) <= max_chars:
            current_line = test_line
        else:
            if current_line:
                lines.append(current_line)
            current_line = word

    if current_line:
        lines.append(current_line)

    return lines

# Usage
result = model.transcribe("audio.mp3", language="th")
whisper_to_srt(result['segments'], "output.srt")

Complete Whisper → SRT Pipeline

def audio_to_srt_complete(
    audio_path: str,
    output_srt_path: str,
    model_name: str = "large-v3",
    language: str = "th",
    initial_prompt: str = None,
    use_vad: bool = True,
    vad_aggressiveness: int = 2
) -> dict:
    """
    Complete pipeline: Audio → Whisper → Clean → SRT

    Args:
        audio_path: Input audio file
        output_srt_path: Output SRT file path
        model_name: Whisper model (default: large-v3)
        language: Language code (default: th)
        initial_prompt: Context for better accuracy
        use_vad: Use VAD to remove silence first
        vad_aggressiveness: VAD level (0-3)

    Returns:
        dict with result and statistics
    """
    import time
    start_time = time.time()

    # Step 1: VAD (optional but recommended)
    if use_vad:
        print("Step 1: Removing silence with VAD...")
        import tempfile
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
            vad_output = tmp.name

        vad_stats = remove_silence_vad(
            audio_path,
            vad_output,
            aggressiveness=vad_aggressiveness
        )
        audio_to_transcribe = vad_output
    else:
        audio_to_transcribe = audio_path
        vad_stats = None

    # Step 2: Transcribe
    print(f"\nStep 2: Transcribing with Whisper {model_name}...")
    result = transcribe_thai_audio(
        audio_to_transcribe,
        model_name=model_name,
        initial_prompt=initial_prompt
    )

    # Step 3: Clean segments
    print("\nStep 3: Cleaning transcript...")
    for segment in result['segments']:
        segment['text'] = clean_whisper_transcript(segment['text'])

    # Update full text
    result['text'] = ' '.join([seg['text'] for seg in result['segments']])

    # Step 4: Generate SRT
    print(f"\nStep 4: Generating SRT → {output_srt_path}...")
    whisper_to_srt(result['segments'], output_srt_path)

    # Cleanup VAD temp file
    if use_vad:
        import os
        os.unlink(vad_output)

    # Statistics
    elapsed_time = time.time() - start_time
    stats = {
        'audio_path': audio_path,
        'output_srt': output_srt_path,
        'model': model_name,
        'language': language,
        'segments_count': len(result['segments']),
        'total_duration_s': result['segments'][-1]['end'] if result['segments'] else 0,
        'processing_time_s': elapsed_time,
        'vad_stats': vad_stats,
        'text_length': len(result['text'])
    }

    print(f"\n✅ Complete!")
    print(f"   Segments: {stats['segments_count']}")
    print(f"   Duration: {stats['total_duration_s']:.2f}s")
    print(f"   Processing time: {elapsed_time:.2f}s")
    if vad_stats:
        print(f"   Time saved by VAD: {vad_stats['reduction_pct']:.1f}%")

    return {
        'result': result,
        'stats': stats
    }

# Usage
result = audio_to_srt_complete(
    audio_path="thai_lesson.mp3",
    output_srt_path="thai_lesson.srt",
    model_name="large-v3",
    language="th",
    initial_prompt="นี่คือการบรรยายเกี่ยวกับ Forex และการเทรด",
    use_vad=True,
    vad_aggressiveness=2
)

print(f"\nTranscript preview:")
print(result['result']['text'][:200] + "...")

---

Production Best Practices

Pre-Transcription Checklist

Before running Whisper, verify:

def validate_audio_file(audio_path: str) -> dict:
    """
    Validate audio file before transcription

    Returns:
        dict with validation results
    """
    from pydub import AudioSegment
    import os

    # Check file exists
    if not os.path.exists(audio_path):
        return {'valid': False, 'error': 'File not found'}

    # Load audio
    try:
        audio = AudioSegment.from_file(audio_path)
    except Exception as e:
        return {'valid': False, 'error': f'Cannot load audio: {e}'}

    # Get specs
    sample_rate = audio.frame_rate
    channels = audio.channels
    duration_s = len(audio) / 1000
    file_size_mb = os.path.getsize(audio_path) / (1024 * 1024)

    # Validation rules
    issues = []
    warnings = []

    # Sample rate check
    if sample_rate < 16000:
        issues.append(f"Sample rate too low: {sample_rate} Hz (recommend ≥16kHz)")
    elif sample_rate < 22050:
        warnings.append(f"Sample rate low: {sample_rate} Hz (recommend ≥22kHz)")

    # Duration check
    if duration_s < 1:
        issues.append(f"Audio too short: {duration_s:.2f}s")
    elif duration_s > 3600:
        warnings.append(f"Audio very long: {duration_s/60:.1f} min (consider chunking)")

    # File size check
    if file_size_mb > 1000:
        warnings.append(f"Large file: {file_size_mb:.1f} MB")

    # Channels
    if channels > 2:
        warnings.append(f"Multi-channel audio ({channels} channels) - will be mixed to mono")

    validation = {
        'valid': len(issues) == 0,
        'audio_path': audio_path,
        'sample_rate': sample_rate,
        'channels': channels,
        'duration_s': duration_s,
        'file_size_mb': file_size_mb,
        'issues': issues,
        'warnings': warnings
    }

    # Print report
    print(f"Audio Validation: {audio_path}")
    print(f"  Sample rate: {sample_rate} Hz")
    print(f"  Channels: {channels}")
    print(f"  Duration: {duration_s:.2f}s ({duration_s/60:.1f} min)")
    print(f"  File size: {file_size_mb:.2f} MB")

    if issues:
        print(f"\n❌ Issues:")
        for issue in issues:
            print(f"   - {issue}")

    if warnings:
        print(f"\n⚠️  Warnings:")
        for warning in warnings:
            print(f"   - {warning}")

    if validation['valid'] and not warnings:
        print(f"\n✅ Audio file is ready for transcription")

    return validation

# Usage
validation = validate_audio_file("input.mp3")
if not validation['valid']:
    print("Cannot proceed - fix issues first!")
else:
    # Proceed with transcription
    result = transcribe_thai_audio("input.mp3")

Quality Control

def quality_check_transcription(result: dict) -> dict:
    """
    Check transcription quality

    Args:
        result: Whisper transcription result

    Returns:
        Quality metrics
    """
    segments = result['segments']

    # Calculate metrics
    total_segments = len(segments)
    total_duration = segments[-1]['end'] if segments else 0
    avg_segment_duration = total_duration / total_segments if total_segments > 0 else 0

    # Check for potential issues
    issues = []

    # 1. Too many short segments (might be noise)
    short_segments = [s for s in segments if (s['end'] - s['start']) < 0.5]
    if len(short_segments) > total_segments * 0.3:
        issues.append(f"High ratio of very short segments ({len(short_segments)}/{total_segments})")

    # 2. Repeated text (hallucination indicator)
    texts = [s['text'] for s in segments]
    unique_texts = set(texts)
    if len(unique_texts) < len(texts) * 0.7:
        issues.append(f"High text repetition (only {len(unique_texts)} unique out of {len(texts)})")

    # 3. Empty or whitespace-only segments
    empty_segments = [s for s in segments if not s['text'].strip()]
    if empty_segments:
        issues.append(f"Found {len(empty_segments)} empty segments")

    # 4. Suspiciously uniform segment lengths (might be chunking artifacts)
    segment_durations = [s['end'] - s['start'] for s in segments]
    avg_duration = sum(segment_durations) / len(segment_durations)
    uniform_count = sum(1 for d in segment_durations if abs(d - avg_duration) < 0.1)
    if uniform_count > len(segments) * 0.8:
        issues.append(f"Segments too uniform ({uniform_count}/{len(segments)}) - check chunking")

    quality = {
        'total_segments': total_segments,
        'total_duration_s': total_duration,
        'avg_segment_duration_s': avg_segment_duration,
        'unique_text_ratio': len(unique_texts) / len(texts) if texts else 0,
        'issues': issues,
        'quality_score': 100 - (len(issues) * 20)  # Simple scoring
    }

    # Print report
    print(f"\n📊 Quality Check:")
    print(f"   Total segments: {total_segments}")
    print(f"   Duration: {total_duration:.2f}s")
    print(f"   Avg segment: {avg_segment_duration:.2f}s")
    print(f"   Unique text ratio: {quality['unique_text_ratio']:.1%}")

    if issues:
        print(f"\n⚠️  Quality Issues:")
        for issue in issues:
            print(f"   - {issue}")
        print(f"\n   Quality score: {quality['quality_score']}/100")
    else:
        print(f"\n✅ No quality issues detected")
        print(f"   Quality score: 100/100")

    return quality

# Usage
result = transcribe_thai_audio("audio.mp3")
quality = quality_check_transcription(result)

if quality['quality_score'] < 60:
    print("\n⚠️  Low quality - consider re-transcribing with different parameters")

---

Platform-Specific Guides

Google Colab

# ===== Google Colab Setup =====

# 1. Check GPU
!nvidia-smi

# 2. Install Whisper
!pip install -q git+https://github.com/openai/whisper.git
!pip install -q pydub webrtcvad

# 3. Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

# 4. Set paths
AUDIO_PATH = "/content/drive/MyDrive/audio/input.mp3"
OUTPUT_SRT = "/content/drive/MyDrive/output/output.srt"

# 5. Transcribe
result = audio_to_srt_complete(
    AUDIO_PATH,
    OUTPUT_SRT,
    model_name="large-v3",
    language="th",
    use_vad=True
)

# 6. Download result
from google.colab import files
files.download(OUTPUT_SRT)

Kaggle

# ===== Kaggle Setup =====

# 1. Check GPU quota
!nvidia-smi

# 2. Install Whisper
!pip install -q openai-whisper pydub webrtcvad

# 3. Input/Output paths
INPUT_DIR = "/kaggle/input/your-dataset/"
OUTPUT_DIR = "/kaggle/working/"

# 4. Transcribe
import os
audio_file = os.path.join(INPUT_DIR, "audio.mp3")
output_srt = os.path.join(OUTPUT_DIR, "output.srt")

result = audio_to_srt_complete(
    audio_file,
    output_srt,
    model_name="large-v3",
    use_vad=True
)

# Note: Kaggle has GPU quota - optimize with batch processing

Paperspace

# ===== Paperspace Setup =====

# 1. Install dependencies
!pip install openai-whisper pydub webrtcvad

# 2. Persistent storage
STORAGE_DIR = "/storage/whisper"
import os
os.makedirs(STORAGE_DIR, exist_ok=True)

# 3. Download model to persistent storage (save quota)
import whisper
model = whisper.load_model("large-v3", download_root=STORAGE_DIR)

# 4. Process files
result = audio_to_srt_complete(
    "/storage/input/audio.mp3",
    "/storage/output/output.srt",
    model_name="large-v3"
)

---

Common Pitfalls & Solutions

Problem 1: Language Switching Mid-Sentence

Symptom:

"นี่คือ trading strategy that works very well for ราคาทอง"
(Thai → English → Thai)

Solution:

# ✅ Force language and use context
result = model.transcribe(
    audio,
    language="th",  # ← Force Thai
    initial_prompt="นี่คือการบรรยายภาษาไทยทั้งหมด ไม่มีภาษาอังกฤษ",
    condition_on_previous_text=True  # ← Use previous context
)

---

Problem 2: Repeated Hallucinations on Silence

Symptom:

Input: [10 seconds of silence]
Output: "ขอบคุณครับ ขอบคุณครับ ขอบคุณครับ..."

Solution:

# ✅ Use VAD to remove silence BEFORE Whisper
result = transcribe_with_vad(
    audio_path,
    vad_aggressiveness=2  # Detect silence better
)

---

Problem 3: Poor Timing in SRT

Symptom: Subtitles appear too early or too late

Solution:

# ✅ Enable word-level timestamps
result = model.transcribe(
    audio,
    word_timestamps=True,  # ← More accurate timing
    prepend_punctuations="\"'"¿([{-",
    append_punctuations="\"'.。,!?:)]}、"
)

---

Problem 4: Out of GPU Memory

Symptom: CUDA out of memory

Solution:

# ✅ Use smaller model or CPU
model = whisper.load_model("medium", device="cpu")

# OR process in smaller chunks
chunks = smart_chunk_audio(audio_path, max_duration_ms=20000)
for chunk in chunks:
    result = model.transcribe(chunk)
    torch.cuda.empty_cache()  # Clean up after each

---

Problem 5: Low Accuracy Despite Using large-v3

Checklist:

• Using language="th" explicitly?
• Using domain-specific initial_prompt?
• Audio quality ≥16kHz?
• Removed silence with VAD?
• Using temperature=0.0 for deterministic output?
• Using fp16=True on GPU?

# ✅ Optimal settings for Thai
result = model.transcribe(
    audio_path,
    language="th",  # ← Must specify
    initial_prompt="นี่คือการบรรยายภาษาไทย...",  # ← Context
    temperature=0.0,  # ← Deterministic
    fp16=True,  # ← GPU optimization
    beam_size=5,  # ← Better accuracy
    best_of=5,  # ← Generate multiple candidates
    word_timestamps=True,  # ← Better timing
    condition_on_previous_text=True  # ← Use context
)

---

Problem 6: Faster-Whisper in Production (Bash Script)

Use Case: Batch transcription in tmux/background

Solution - Faster-Whisper Bash Script:

#!/bin/bash
# batch_transcribe_faster.sh

set -e

INPUT_DIR="/path/to/videos"
OUTPUT_DIR="/path/to/output"
WHISPER_MODEL="large-v3"

mkdir -p "$OUTPUT_DIR"

FILES=(
    "video1.mp4"
    "video2.mp4"
    "video3.mp4"
)

for file in "${FILES[@]}"; do
    input_file="$INPUT_DIR/$file"
    basename="${file%.mp4}"

    echo ">>> Processing: $file"

    # Run Faster-Whisper via embedded Python
    python3 << PYTHON
from faster_whisper import WhisperModel
import json
from pathlib import Path

# Load model
print("Loading Faster-Whisper model...")
model = WhisperModel("${WHISPER_MODEL}", device="cuda", compute_type="int8")

# Transcribe
print("Transcribing: ${input_file}")
segments, info = model.transcribe(
    "${input_file}",
    language="th",
    beam_size=5,
    word_timestamps=True
)

# Convert to JSON format (compatible with OpenAI Whisper)
result = {
    "text": "",
    "segments": [],
    "language": "th"
}

for segment in segments:
    result["text"] += segment.text + " "
    result["segments"].append({
        "id": segment.id,
        "seek": segment.seek,
        "start": segment.start,
        "end": segment.end,
        "text": segment.text,
        "tokens": segment.tokens,
        "temperature": segment.temperature,
        "avg_logprob": segment.avg_logprob,
        "compression_ratio": segment.compression_ratio,
        "no_speech_prob": segment.no_speech_prob,
        "words": [
            {
                "word": word.word,
                "start": word.start,
                "end": word.end,
                "probability": word.probability
            }
            for word in (segment.words or [])
        ] if segment.words else []
    })

# Save JSON
output_json = Path("${OUTPUT_DIR}") / "${basename}.json"
with open(output_json, "w", encoding="utf-8") as f:
    json.dump(result, f, ensure_ascii=False, indent=2)

print(f"Saved: {output_json}")
print(f"Duration: {info.duration:.2f}s")
print(f"Language: {info.language} ({info.language_probability:.2%})")
PYTHON

    echo "✅ SUCCESS: $file"
done

echo "All Done!"

Run in tmux:

# 1. Start tmux session
tmux new -s transcribe

# 2. Run script
bash batch_transcribe_faster.sh

# 3. Detach from tmux (close browser safely)
# Press: Ctrl+B then D

# 4. Check progress later
tmux attach -s transcribe

# 5. Check output
ls -lh /path/to/output/

Why this works:

• ✅ 4-5x faster than OpenAI Whisper
• ✅ 62% less RAM (4 GB vs 10 GB)
• ✅ Same accuracy (95%+ for Thai)
• ✅ Compatible format (same JSON as OpenAI Whisper)
• ✅ Runs in background (tmux detach)
• ✅ Word-level timestamps included

---

Summary: Production Workflow

Option 1: Faster-Whisper (Recommended) ⚡

# ===== FASTER-WHISPER PRODUCTION WORKFLOW =====

from faster_whisper import WhisperModel
import json
from pathlib import Path

def faster_whisper_production(
    audio_path: str,
    output_json: str,
    language: str = "th",
    model_name: str = "large-v3"
) -> dict:
    """
    Production-ready Faster-Whisper transcription.
    4-5x faster than OpenAI Whisper with same accuracy.
    """

    # Load model (INT8 for speed)
    model = WhisperModel(
        model_name,
        device="cuda",
        compute_type="int8"  # 4-5x faster, 62% less RAM
    )

    # Transcribe with optimal settings
    segments, info = model.transcribe(
        audio_path,
        language=language,
        beam_size=5,
        word_timestamps=True,
        vad_filter=True,  # Remove silence
        vad_parameters=dict(
            threshold=0.5,
            min_speech_duration_ms=250,
            min_silence_duration_ms=2000
        )
    )

    # Convert to OpenAI Whisper format
    result = {
        "text": "",
        "segments": [],
        "language": info.language
    }

    for segment in segments:
        result["text"] += segment.text + " "
        result["segments"].append({
            "id": segment.id,
            "start": segment.start,
            "end": segment.end,
            "text": segment.text,
            "words": [
                {
                    "word": word.word,
                    "start": word.start,
                    "end": word.end,
                    "probability": word.probability
                }
                for word in (segment.words or [])
            ] if segment.words else []
        })

    # Save JSON
    with open(output_json, "w", encoding="utf-8") as f:
        json.dump(result, f, ensure_ascii=False, indent=2)

    return result

# Usage
result = faster_whisper_production(
    "video.mp4",
    "output.json",
    language="th"
)

Option 2: OpenAI Whisper (Standard)

# ===== OPENAI WHISPER PRODUCTION WORKFLOW =====

def production_transcription_pipeline(
    audio_path: str,
    output_srt: str,
    language: str = "th",
    domain_context: str = None
) -> dict:
    """
    Production-ready transcription pipeline

    Steps:
    1. Validate audio file
    2. Remove silence (VAD)
    3. Transcribe with Whisper
    4. Clean hallucinations
    5. Generate SRT
    6. Quality check

    Args:
        audio_path: Input audio file
        output_srt: Output SRT file
        language: Language code (default: th)
        domain_context: Context for initial_prompt

    Returns:
        Complete results with quality metrics
    """
    # Step 1: Validate
    print("=" * 60)
    print("STEP 1: Validating audio file...")
    print("=" * 60)
    validation = validate_audio_file(audio_path)
    if not validation['valid']:
        return {'error': 'Validation failed', 'validation': validation}

    # Step 2: Prepare initial prompt
    if domain_context:
        initial_prompt = f"นี่คือการบรรยายเกี่ยวกับ {domain_context} ผู้บรรยายพูดภาษาไทยชัดเจน"
    else:
        initial_prompt = "นี่คือการบรรยายภาษาไทย ผู้บรรยายพูดชัดเจน"

    # Step 3: Transcribe with all optimizations
    print("\n" + "=" * 60)
    print("STEP 2: Transcribing with Whisper...")
    print("=" * 60)
    result = audio_to_srt_complete(
        audio_path=audio_path,
        output_srt_path=output_srt,
        model_name="large-v3",
        language=language,
        initial_prompt=initial_prompt,
        use_vad=True,
        vad_aggressiveness=2
    )

    # Step 4: Quality check
    print("\n" + "=" * 60)
    print("STEP 3: Quality check...")
    print("=" * 60)
    quality = quality_check_transcription(result['result'])

    # Final report
    print("\n" + "=" * 60)
    print("✅ TRANSCRIPTION COMPLETE")
    print("=" * 60)
    print(f"Input: {audio_path}")
    print(f"Output: {output_srt}")
    print(f"Segments: {result['stats']['segments_count']}")
    print(f"Duration: {result['stats']['total_duration_s']:.2f}s")
    print(f"Processing time: {result['stats']['processing_time_s']:.2f}s")
    print(f"Quality score: {quality['quality_score']}/100")

    return {
        'result': result['result'],
        'stats': result['stats'],
        'quality': quality,
        'validation': validation
    }

# ===== USAGE =====

# Simple usage
result = production_transcription_pipeline(
    audio_path="thai_forex_lesson.mp3",
    output_srt="thai_forex_lesson.srt",
    language="th",
    domain_context="Forex การเทรด และการลงทุน"
)

# Check results
if 'error' not in result:
    print(f"\n✅ Success! SRT saved to: {result['stats']['output_srt']}")
    print(f"Preview: {result['result']['text'][:200]}...")
else:
    print(f"\n❌ Error: {result['error']}")

---

Quick Reference

Essential Parameters

# Minimal (fastest)
result = model.transcribe(audio_path)

# Recommended for Thai
result = model.transcribe(
    audio_path,
    language="th",
    initial_prompt="นี่คือการบรรยายภาษาไทย"
)

# Production (best quality)
result = model.transcribe(
    audio_path,
    language="th",
    initial_prompt="นี่คือการบรรยายเกี่ยวกับ [domain]",
    temperature=0.0,
    fp16=True,
    beam_size=5,
    best_of=5,
    word_timestamps=True,
    condition_on_previous_text=True,
    no_speech_threshold=0.6,
    logprob_threshold=-1.0
)

Model Comparison Quick Guide

Need	Model	Why
Testing	base	Fast, good enough for testing
English draft	small	Fast, 92%+ accuracy
Thai draft	medium	Minimum for acceptable Thai
Thai production	large-v3	95%+ accuracy
Multilingual	large-v3	Best language detection

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Last Updated: 2025-10-24 Version: 1.0 Lines: 1,600+ Status: Production Ready ✅