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Imagine1A/
Translation

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Complete Video Translation Pipeline using NeMo ASR, NLLB, and TTS

A comprehensive Python-based pipeline for translating video content from one language to another. Extract audio → Transcribe (STT) → Translate → Synthesize (TTS) → Recompose video with new audio.

Overview

This project consists of six main modules:

Master Pipeline:

video_translation_pipeline.py - Orchestrates complete video translation workflow

Core Modules:

audio_extraction_from_video.py - Extracts audio tracks from video files
audio_to_asr_nemotron.py - Transcribes extracted audio using NeMo ASR (Speech-to-Text)
text_to_speech_tts.py - Converts text to speech using NeMo TTS models (Text-to-Speech)
nllb_translation.py - Translates text to other languages using NLLB model
download_model_nemotron.py - Downloads pre-trained Nemotron ASR model
download_model_nllb.py - Downloads NLLB (No Language Left Behind) translation model

Files Description

1. `audio_extraction_from_video.py`

Purpose: Extract audio from video files and save as standalone audio files.

Key Features:

Supports multiple video formats (MP4, AVI, MKV, MOV, FLV, WMV, WebM, M4V)
Configurable audio codec and bitrate
Automatic output directory creation
Comprehensive error handling and validation
Resource management with guaranteed cleanup
Detailed logging for debugging

Function Signature:

python

extract_audio_from_video(
    video_file_path,           # Path to input video
    audio_file_path,           # Path to save audio
    audio_codec='libmp3lame',  # Audio codec (default: MP3)
    audio_bitrate='192k',      # Audio bitrate (default: 192kbps)
    verbose=False              # Show moviepy output
)

Usage Examples:

bash

# Basic usage with defaults (MP3, 192kbps)
python audio_extraction_from_video.py input_video.mp4 output_audio.mp3

# Custom codec and bitrate
python audio_extraction_from_video.py video.mp4 audio.wav libsndfile 320k

# In Python
from audio_extraction_from_video import extract_audio_from_video
extract_audio_from_video("movie.mkv", "soundtrack.mp3", audio_bitrate='256k')

Improvements Made:

✓ Validates video file existence and format
✓ Checks for audio track presence before processing
✓ Proper exception handling with informative error messages
✓ Guaranteed resource cleanup with try-finally blocks
✓ Logging system for tracking extraction progress
✓ Configurable output parameters for flexibility
✓ Command-line interface support with optional parameters

2. `audio_to_asr_nemotron.py`

Purpose: Transcribe audio files using NeMo's Conformer CTC ASR model (English only).

Key Features:

Model caching to avoid expensive reloads
Automatic GPU detection for faster inference
Audio format validation (MP3, WAV, FLAC, OGG, M4A)
Comprehensive error handling and logging
Optional output file saving
Command-line argument support

Function Signature:

python

transcribe_audio(
    audio_file_path  # Path to audio file to transcribe
)

Usage Examples:

bash

# Basic usage - print to console
python audio_to_asr_nemotron.py input_video.mp4

# Save transcription to file
python audio_to_asr_nemotron.py input_video.mp4 audio.mp3 transcription.txt

# In Python
from audio_to_asr_nemotron import transcribe_audio, get_asr_model

# First call loads model, subsequent calls use cached model
text1 = transcribe_audio("audio1.mp3")
text2 = transcribe_audio("audio2.mp3")  # Uses cached model

Improvements Made:

✓ Single model loading with caching (eliminates repeated load overhead)
✓ GPU support for accelerated inference
✓ Audio file validation before transcription
✓ Graceful error handling with detailed logging
✓ Flexible output options (console or file)
✓ Better formatted output with visual separators
✓ Removed duplicate imports
✓ Refactored into reusable functions

3. `text_to_speech_tts.py`

Purpose: Convert text to natural-sounding speech audio using NeMo TTS (Text-to-Speech) models.

Key Features:

Support for multiple TTS models (Glow-TTS, FastPitch)
Vocoder support (HiFi-GAN for high-quality audio)
Pitch and speed control
Model caching for efficient reuse
GPU acceleration for faster synthesis
Batch processing for multiple texts
Comprehensive error handling and validation
Detailed logging for debugging

Function Signature:

python

text_to_speech(
    text,                    # Text to convert to speech
    output_audio_path,       # Path to save audio file
    model_name='glow_tts',   # TTS model ('glow_tts' or 'fastpitch')
    vocoder_name='hifigan',  # Vocoder model
    speaker=None,            # Speaker ID (for multi-speaker models)
    pitch=1.0,               # Pitch adjustment (0.5-2.0)
    speed=1.0                # Speed adjustment (0.5-2.0)
)

Usage Examples:

bash

# Basic text-to-speech
python text_to_speech_tts.py "Hello, this is a test." output.wav

# Custom model
python text_to_speech_tts.py "Hello world" output.wav fastpitch

# Batch processing in Python
from text_to_speech_tts import batch_text_to_speech

texts = ["Hello world", "How are you?", "Goodbye"]
outputs = ["hello.wav", "how.wav", "goodbye.wav"]
batch_text_to_speech(texts, outputs)

Advanced Usage:

python

from text_to_speech_tts import text_to_speech

# Adjust pitch and speed
text_to_speech(
    "Make this sound higher and faster",
    "output.wav",
    pitch=1.3,      # 30% higher pitch
    speed=1.2       # 20% faster
)

# Use FastPitch model (better control)
text_to_speech(
    "This uses FastPitch",
    "output.wav",
    model_name="fastpitch"
)

Model Comparison:

Feature	Glow-TTS	FastPitch
Speed	Fast	Fast
Quality	Good	Excellent
Pitch Control	Basic	Advanced
Duration Control	Limited	Full
Naturalness	Good	Very Good

Supported Output Formats:

WAV (default, high quality)
MP3, OGG, FLAC (via torchaudio)

Improvements Made:

✓ Model and vocoder caching for efficiency
✓ GPU acceleration with automatic CPU fallback
✓ Input validation (text length, parameter ranges)
✓ Batch processing support
✓ Detailed control over pitch and speed
✓ Comprehensive logging and error handling
✓ Automatic output directory creation

4. `download_model_nemotron.py`

Purpose: Download the pre-trained Nemotron-3.5 ASR streaming model from Hugging Face.

Features:

Downloads Nemotron-3.5 ASR Streaming 0.6b model
Saves to nemotron-3.5-asr-streaming-0.6b/ directory
Model weights: ~600MB (distilled for efficiency)
Includes model configuration, tokenizer, and documentation

Model Details:

Architecture: Conformer CTC
Language: English
Streaming Support: Yes (processes audio chunks in real-time)
Quantization: Distilled 600M parameter model

5. `download_model_nllb.py`

Purpose: Download the NLLB-200 translation model for multi-language translation.

Features:

Downloads NLLB-200 distilled model
Supports 200+ languages
Model size: ~600MB
Enables machine translation capabilities

6. `nllb_translation.py`

Purpose: Translate text between 200+ languages using Meta's NLLB-200 model.

Key Features:

Supports 200+ languages with high-quality translation
Model caching for efficiency
GPU acceleration with automatic CPU fallback
Batch translation support
Input validation and error handling
Comprehensive logging
Multiple model variants available

Function Signature:

python

translate_text(
    text,                                          # Text to translate
    source_lang='english',                         # Source language
    target_lang='spanish',                         # Target language
    model_name='facebook/nllb-200-distilled-600M', # Model to use
    max_length=512                                 # Max output length
)

Supported Languages (200+ total): Afrikaans, Amharic, Arabic, Bengali, Bulgarian, Catalan, Chinese (Simplified & Traditional), Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Javanese, Korean, Lithuanian, Marathi, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and many more.

Usage Examples:

bash

# Simple translation
python nllb_translation.py "Hello, how are you?" english spanish

# Different language pair
python nllb_translation.py "Bonjour le monde" french english

# From German to Japanese
python nllb_translation.py "Guten Tag" german japanese

Python API:

python

from nllb_translation import translate_text, batch_translate

# Single translation
result = translate_text("Hello world", "english", "spanish")
print(result)  # "Hola mundo"

# Batch translation
texts = ["Hello", "Good morning", "How are you?"]
results = batch_translate(texts, "english", "french")

# Use different model (higher quality)
result = translate_text(
    "Complex text to translate",
    "english",
    "german",
    model_name="facebook/nllb-200-1.3B"  # Better quality, slower
)

# List all supported languages
from nllb_translation import list_supported_languages
languages = list_supported_languages()
print(languages)

Model Variants:

Model	Size	Speed	Quality	Use Case
nllb-200-distilled-600M	~600MB	Fast	Good	Real-time, resource-constrained
nllb-200-1.3B	~2.6GB	Medium	Very Good	Balanced quality/speed
nllb-200-3.3B	~6.5GB	Slow	Excellent	Maximum quality

Improvements Made:

✓ Comprehensive language support dictionary (200+)
✓ Model caching to avoid reloads
✓ GPU acceleration with automatic CPU fallback
✓ Language code validation and conversion
✓ Input validation (text length, language pairs)
✓ Batch processing for multiple texts
✓ Detailed logging for debugging
✓ Multiple model variants support
✓ Command-line interface support

7. `video_translation_pipeline.py` (MASTER ORCHESTRATOR)

Purpose: Master script that orchestrates the complete video translation workflow, coordinating all modules in sequence.

What it Does:

Video Input
    ↓
Extract Audio (moviepy)
    ↓
Transcribe Audio (Nemotron ASR)
    ↓
Translate Text (NLLB-200)
    ↓
Synthesize Speech (NeMo TTS)
    ↓
Attach Audio to Video (moviepy)
    ↓
Video Output with Translated Audio

Key Features:

Orchestrates all components seamlessly
Comprehensive logging for each step
Automatic GPU detection and utilization
Intermediate file management (save or cleanup)
Error handling with clear diagnostic messages
Progress reporting with elapsed time

Function Signature:

python

translate_video(
    input_video_path,                              # Path to input video
    output_video_path,                             # Path to output video
    source_language='english',                     # Source language
    target_language='spanish',                     # Target language
    translation_model='facebook/nllb-200-distilled-600M',  # Translation model
    tts_model='glow_tts',                          # TTS model
    keep_intermediate_files=False                  # Keep temp files
)

Helper Function:

python

attach_audio_to_video(
    video_path,     # Input video
    audio_path,     # New audio to attach
    output_path,    # Output video path
    fps=24          # Output frames per second
)

Key Improvements:

✓ Complete end-to-end automation
✓ Detailed step-by-step logging
✓ Automatic audio/video synchronization
✓ GPU acceleration detection
✓ Flexible model selection
✓ Intermediate file management
✓ Comprehensive error messages

Complete Pipeline Usage

⭐ MASTER PIPELINE: Complete Video Translation (One Command!)

bash

# Translate English video to Spanish
python video_translation_pipeline.py input_video.mp4 output_spanish.mp4 english spanish

# Translate to German with better quality
python video_translation_pipeline.py input.mp4 output_german.mp4 english german fastpitch

# Use higher quality translation model
python video_translation_pipeline.py input.mp4 output.mp4 english spanish facebook/nllb-200-1.3B

# Keep intermediate files for debugging
python video_translation_pipeline.py input.mp4 output.mp4 english spanish --keep

What this does:

✓ Extracts audio from video
✓ Transcribes audio to text (STT)
✓ Translates text to target language
✓ Converts translated text to speech (TTS)
✓ Attaches new audio to video
✓ Saves translated video with synchronized audio

Output:

Fully translated video with synchronized audio
Optional: Intermediate files (transcription, translation, synthesized audio)

Video Translation Pipeline - Advanced Usage

python

from video_translation_pipeline import translate_video

# Translate video with custom settings
translate_video(
    input_video_path="english_lecture.mp4",
    output_video_path="spanish_lecture.mp4",
    source_language="english",
    target_language="spanish",
    translation_model="facebook/nllb-200-1.3B",  # Higher quality
    tts_model="fastpitch",                        # Better TTS
    keep_intermediate_files=True                  # Keep all files
)

Video Translation Options

Parameter	Options	Default	Description
Source Language	Any supported language	english	Language of input video
Target Language	Any supported language	spanish	Language for output video
TTS Model	glow_tts, fastpitch	glow_tts	Text-to-speech model (fastpitch = better quality)
Translation Model	facebook/nllb-200-distilled-600M, facebook/nllb-200-1.3B, facebook/nllb-200-3.3B	distilled-600M	NLLB model (larger = better quality, slower)

Full Video-to-Transcription Pipeline:

bash

# One-command pipeline
python audio_to_asr_nemotron.py input_video.mp4 output_audio.mp3 transcription.txt

This single command:

Loads the pre-trained ASR model (or uses cached version)
Extracts audio from the video
Transcribes the audio
Saves the transcription to a file

Text-to-Speech Pipeline:

bash

# Simple text to speech
python text_to_speech_tts.py "Hello, welcome to the audio translation pipeline" output.wav

# With custom voice characteristics
python text_to_speech_tts.py "This sounds more natural" output.wav fastpitch

Complete Audio-to-Text-to-Speech Pipeline:

python

from audio_extraction_from_video import extract_audio_from_video
from audio_to_asr_nemotron import transcribe_audio
from text_to_speech_tts import text_to_speech

# Step 1: Extract audio from video
extract_audio_from_video("my_video.mp4", "extracted_audio.mp3")

# Step 2: Transcribe to text
transcription = transcribe_audio("extracted_audio.mp3")
print(f"Transcription: {transcription}")

# Step 3: Convert text back to speech
text_to_speech(transcription, "recreated_speech.wav")

Complete Video-to-Translation-to-Speech Pipeline (Multi-Language):

python

from audio_extraction_from_video import extract_audio_from_video
from audio_to_asr_nemotron import transcribe_audio
from nllb_translation import translate_text
from text_to_speech_tts import text_to_speech

# Step 1: Extract audio from video
extract_audio_from_video("english_video.mp4", "audio.mp3")

# Step 2: Transcribe audio to English text
transcription = transcribe_audio("audio.mp3")
print(f"English: {transcription}")

# Step 3: Translate to Spanish
translation = translate_text(transcription, "english", "spanish")
print(f"Spanish: {translation}")

# Step 4: Convert Spanish text to speech
text_to_speech(translation, "spanish_speech.wav")

# Result: Spanish audio file from English video!

Batch Translation Pipeline:

python

from nllb_translation import batch_translate

# Translate multiple lines
english_texts = [
    "Hello, welcome to our service",
    "How can we help you today?",
    "Thank you for choosing us"
]

# Translate to multiple languages
for target_lang in ["spanish", "french", "german"]:
    translations = batch_translate(english_texts, "english", target_lang)
    for i, (original, translated) in enumerate(zip(english_texts, translations)):
        print(f"{target_lang.upper()} {i+1}: {translated}")

Complete Multilingual Pipeline (Video → 3 Languages → Audio):

python

from audio_extraction_from_video import extract_audio_from_video
from audio_to_asr_nemotron import transcribe_audio
from nllb_translation import translate_text
from text_to_speech_tts import text_to_speech

# Extract and transcribe
extract_audio_from_video("input_video.mp4", "audio.mp3")
english_text = transcribe_audio("audio.mp3")

# Translate and create audio for multiple languages
target_languages = ["spanish", "french", "german"]

for lang in target_languages:
    # Translate
    translated = translate_text(english_text, "english", lang)
    
    # Convert to speech
    output_file = f"output_{lang}.wav"
    text_to_speech(translated, output_file)
    
    print(f"✓ Created {output_file}")

Step-by-Step Usage:

python

from audio_extraction_from_video import extract_audio_from_video
from audio_to_asr_nemotron import transcribe_audio

# Step 1: Extract audio
extract_audio_from_video("my_video.mp4", "my_audio.mp3")

# Step 2: Transcribe audio
transcription = transcribe_audio("my_audio.mp3")
print(transcription)

Installation & Setup

Dependencies:

bash

# Install required packages
pip install nemo-toolkit torch moviepy torchaudio

# Or with conda (recommended for GPU support)
conda install pytorch torchvision torchaudio pytorch-cuda -c pytorch
pip install nemo-toolkit moviepy

Note for TTS: The torchaudio package is required for text-to-speech functionality. Make sure it's installed.

GPU Setup (Optional but Recommended):

For NVIDIA GPUs, ensure CUDA is installed:

bash

# Check CUDA availability
python -c "import torch; print(torch.cuda.is_available())"

Download Models:

bash

# Download Nemotron ASR model
python download_model_nemotron.py

# Download NLLB translation model (if needed)
python download_model_nllb.py

🚀 Quick Start

Basic video translation (30 seconds of setup):

bash

# 1. Install dependencies (one time)
pip install nemo-toolkit torch moviepy torchaudio transformers

# 2. Run the complete pipeline
python video_translation_pipeline.py input_video.mp4 output_video.mp4 english spanish

That's it! Your translated video will be saved to output_video.mp4.

Python API (for scripts):

python

from video_translation_pipeline import translate_video

# Translate English video to Spanish
translate_video(
    "input_video.mp4",
    "output_spanish.mp4", 
    "english",
    "spanish"
)

Try different languages:

bash

# English → German
python video_translation_pipeline.py video.mp4 video_german.mp4 english german

# English → French
python video_translation_pipeline.py video.mp4 video_french.mp4 english french

# English → Japanese
python video_translation_pipeline.py video.mp4 video_japanese.mp4 english japanese

# English → Mandarin Chinese
python video_translation_pipeline.py video.mp4 video_chinese.mp4 english chinese_simplified

Error Handling

All functions include comprehensive error handling:

Common Errors and Solutions:

Error	Cause	Solution
`FileNotFoundError`	Video/audio file doesn't exist	Check file path and ensure file exists
`ValueError: Unsupported format`	Invalid file format	Use supported formats (MP4, WAV, etc.)
`ValueError: Video has no audio`	Video has no audio track	Ensure video has audio stream
`RuntimeError: Could not load ASR model`	Model download failed	Check internet connection, try again
CUDA out of memory	GPU memory insufficient	Use CPU or reduce batch size

Logging:

All operations produce timestamped logs:

2026-07-07 10:30:15,123 - INFO - Loading video: input.mp4
2026-07-07 10:30:16,456 - INFO - Audio duration: 120.50 seconds
2026-07-07 10:30:45,789 - INFO - Audio extraction completed successfully

Performance Notes

Model Caching:

First transcription call loads the model (~1-2 minutes)
Subsequent calls reuse cached model (~seconds per audio file)

Audio Extraction:

Speed depends on video size and codec
Typically 1-5x real-time speed

Transcription:

GPU: ~0.2-0.5x real-time (faster than playback)
CPU: ~1-3x real-time

Example Performance:

120-second audio file:
- Extraction:    ~30-60 seconds
- Transcription: ~30-60 seconds (GPU) or ~120-360 seconds (CPU)
- Total:         ~2-5 minutes

Advanced Usage

Custom Audio Quality:

python

# High-quality MP3 (256kbps)
extract_audio_from_video("video.mp4", "audio.mp3", audio_bitrate='256k')

# WAV format (lossless)
extract_audio_from_video("video.mp4", "audio.wav", audio_codec='pcm_s16le')

# FLAC (lossless compression)
extract_audio_from_video("video.mp4", "audio.flac", audio_codec='flac')

Batch Processing:

python

from pathlib import Path
from audio_to_asr_nemotron import transcribe_audio

# Process all MP4 files in directory
video_dir = Path("./videos")
for video_file in video_dir.glob("*.mp4"):
    audio_file = f"audio_{video_file.stem}.mp3"
    output_file = f"transcripts_{video_file.stem}.txt"
    
    try:
        from audio_extraction_from_video import extract_audio_from_video
        extract_audio_from_video(str(video_file), audio_file)
        transcription = transcribe_audio(audio_file)
        
        with open(output_file, 'w') as f:
            f.write(transcription)
        print(f"✓ Processed: {video_file}")
    except Exception as e:
        print(f"✗ Error processing {video_file}: {e}")

Architecture & Design Improvements

Original Code Issues Addressed:

Issue	Problem	Solution
Duplicate imports	Code redundancy	Removed duplicates
Model reloading	Performance overhead	Implemented global model cache
No error handling	Silent failures	Added try-except blocks
No input validation	Crashes on bad input	Validate files before processing
Poor logging	Hard to debug	Added comprehensive logging
Hardcoded paths	Inflexible usage	Added command-line argument support
Resource leaks	Memory issues	Proper cleanup in try-finally
No documentation	Usage unclear	Detailed docstrings and README

Code Quality Improvements:

✓ Performance: Model caching, GPU support ✓ Reliability: Error handling, resource cleanup, validation ✓ Maintainability: Clear code structure, logging, documentation ✓ Usability: CLI support, flexible parameters, informative messages ✓ Extensibility: Modular design for easy integration with other tools

Supported Languages (200+)

The NLLB translation module supports 200+ languages. Here are the most commonly used:

European: English, Spanish, French, German, Italian, Portuguese, Dutch, Polish, Russian, Ukrainian, Greek, Turkish, Swedish, Norwegian, Danish, Finnish, Czech, Slovak, Hungarian, Romanian, Bulgarian, Catalan

Asian: Chinese (Simplified & Traditional), Japanese, Korean, Thai, Vietnamese, Indonesian, Javanese, Malay, Tagalog, Burmese

South Asian: Hindi, Bengali, Tamil, Telugu, Gujarati, Marathi, Punjabi, Urdu

Middle Eastern & African: Arabic, Hebrew, Persian, Turkish, Amharic, Somali, Swahili, Yoruba

Usage:

bash

# Get a full list in your code
from nllb_translation import list_supported_languages
languages = list_supported_languages()
print(languages)

Language Codes: For command line, use the English name (lowercase):

bash

python video_translation_pipeline.py input.mp4 output.mp4 english german
python video_translation_pipeline.py input.mp4 output.mp4 french japanese
python video_translation_pipeline.py input.mp4 output.mp4 spanish chinese_simplified

Supported Formats

Video Formats:

MP4, AVI, MKV, MOV, FLV, WMV, WebM, M4V

Audio Formats (Extraction Output):

MP3 (default), WAV, FLAC, OGG, M4A, and others supported by ffmpeg

Audio Formats (Transcription Input):

MP3, WAV, FLAC, OGG, M4A

Troubleshooting

Issue: "Module not found: nemo"

pip install nemo-toolkit

Issue: "CUDA out of memory"

The code will automatically fall back to CPU. No action needed.

Issue: Slow extraction

Check disk I/O performance
Ensure sufficient disk space
Consider using SSD instead of HDD

Issue: Poor transcription quality

Check audio quality and volume levels
Ensure audio is in English (Nemotron is English-only)
Try different audio formats

Future Enhancements

Text-to-Speech conversion (TTS) with NeMo models
Multi-language support with NLLB integration
Batch processing with progress bars
Audio preprocessing (noise reduction, volume normalization)
WebUI for easier interaction
Timestamp-based transcription (word-level timing)
Speaker diarization
Export to various formats (SRT, VTT subtitles)
Integration with translation pipelines
Real-time streaming transcription and TTS

License & Attribution

NeMo: NVIDIA NeMo (Apache 2.0)
MoviePy: MoviePy (MIT License)
Nemotron Model: NVIDIA's Nemotron model
NLLB Model: Meta's NLLB model

Support & Debugging

Enable Verbose Logging:

python

import logging
logging.basicConfig(level=logging.DEBUG)  # Show all debug messages

extract_audio_from_video("video.mp4", "audio.mp3", verbose=True)

Check System Info:

python

import torch
print(f"CUDA Available: {torch.cuda.is_available()}")
print(f"CUDA Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU'}")
print(f"PyTorch Version: {torch.__version__}")

Contact & Contributing

For issues or feature requests, please refer to the respective project repositories:

NeMo: https://github.com/NVIDIA/NeMo
MoviePy: https://github.com/Zulko/moviepy
NLLB: https://github.com/facebookresearch/nllb

Last Updated: 2026-07-07 Version: 2.0 (Refactored & Enhanced)

Imagine1A/Translation

Complete Video Translation Pipeline using NeMo ASR, NLLB, and TTS

Overview

Files Description

1. audio_extraction_from_video.py

2. audio_to_asr_nemotron.py

3. text_to_speech_tts.py

4. download_model_nemotron.py

5. download_model_nllb.py

6. nllb_translation.py

7. video_translation_pipeline.py (MASTER ORCHESTRATOR)

Complete Pipeline Usage

⭐ MASTER PIPELINE: Complete Video Translation (One Command!)

Video Translation Pipeline - Advanced Usage

Video Translation Options

Full Video-to-Transcription Pipeline:

Text-to-Speech Pipeline:

Complete Audio-to-Text-to-Speech Pipeline:

Complete Video-to-Translation-to-Speech Pipeline (Multi-Language):

Batch Translation Pipeline:

Complete Multilingual Pipeline (Video → 3 Languages → Audio):

Step-by-Step Usage:

Installation & Setup

Dependencies:

GPU Setup (Optional but Recommended):

Download Models:

🚀 Quick Start

Error Handling

Common Errors and Solutions:

Logging:

Performance Notes

Model Caching:

Audio Extraction:

Transcription:

Example Performance:

Advanced Usage

Custom Audio Quality:

Batch Processing:

Architecture & Design Improvements

Original Code Issues Addressed:

Code Quality Improvements:

Supported Languages (200+)

Supported Formats

Video Formats:

Audio Formats (Extraction Output):

Audio Formats (Transcription Input):

Troubleshooting

Issue: "Module not found: nemo"

Issue: "CUDA out of memory"

Issue: Slow extraction

Issue: Poor transcription quality

Future Enhancements

License & Attribution

Support & Debugging

Enable Verbose Logging:

Check System Info:

Contact & Contributing

Imagine1A/
Translation

1. `audio_extraction_from_video.py`

2. `audio_to_asr_nemotron.py`

3. `text_to_speech_tts.py`

4. `download_model_nemotron.py`

5. `download_model_nllb.py`

6. `nllb_translation.py`

7. `video_translation_pipeline.py` (MASTER ORCHESTRATOR)