Extract Audio from Video & Convert to Text in C++

extract-audio-from-video.md

🧱 PROJECT MODULES: "Extract Audio from Video & Convert to Text in C++"

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🎬 Module 1: Extract Audio from Video

Goal: Extract audio track from a video file.

Tasks:

• Read video file: Implement a simple video file reader or work with basic file formats (e.g., .avi, .mp4).
• Extract audio stream: Identify and extract the audio stream from the video. This involves parsing the container format (e.g., MP4, AVI, MKV).
• Save as audio file: Convert the extracted audio into a separate audio file format (e.g., .wav, .mp3).

Note: For this module, even though you want to do everything manually, it’s quite complex to handle formats like MP4 or MKV from scratch in C++. For simplicity, we’d typically use libraries like FFmpeg for this part, but since you want to avoid libraries, you’d need to implement custom parsers for audio extraction, which would be a significant challenge.

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🎧 Module 2: Process Audio Data

Goal: Convert the raw audio data into a suitable format for speech recognition.

Tasks:

• Understand audio encoding: Study basic audio file formats (e.g., .wav, .mp3, PCM).
• Parse audio: Read the audio data and break it into samples (this involves converting the raw bytes into useful audio data).
• Convert audio to mono (optional): Simplify audio by converting stereo to mono, reducing complexity.
• Normalize audio: Adjust the volume levels to ensure consistency across the entire file.

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🧠 Module 3: Speech-to-Text (Simple Transcription)

Goal: Convert speech in the audio to text.

Tasks:

• Audio pre-processing: Apply simple noise reduction or filtering (e.g., high-pass filters to remove background hum).

• Build a basic speech recognizer:

  • Research phonetic models: Basic speech-to-text works by matching sound patterns to phonetic symbols or pre-defined models.
  • Create a basic lookup table: Match common audio patterns to words.
  • Use FFT (Fast Fourier Transform): Convert audio signals into frequency-domain, breaking speech into phonemes (small sound units).
  • Map phonemes to words: Use a predefined dictionary of phonemes/words for simple transcription.

This would be a very rudimentary speech recognition system, likely with very limited accuracy unless you integrate a trained model, but it’s feasible to start simple.

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📄 Module 4: Output Transcribed Text

Goal: Output the transcribed text to the user.

Tasks:

• Display the text: Print the text output to the console or show it on a GUI.
• Save the text: Write the transcribed text to a .txt file for later use.
• Timestamp text (optional): If you want time-based text, include the timestamp of each word or sentence.

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⚡ Module 5: Basic User Interface (Optional)

Goal: Create a simple way for users to interact with the application.

Tasks:

• Command-line interface: Accept video input file, output file options, and other settings via command-line arguments.
• Error handling: Provide useful error messages (e.g., if the file isn’t a valid video or audio).

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⚙️ Module 6: Testing & Optimization

Goal: Test the application on various video and audio inputs.

Tasks:

• Test on different video formats: MP4, MKV, AVI.
• Test on different speech types: Try clear speech, noisy environments, accents, and multiple speakers.
• Optimize audio processing: Reduce latency, improve transcription accuracy, and optimize performance.

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🧰 Tools & Techniques

Task	Technology/Technique
Extract Audio from Video	Raw file parsing, or possibly FFmpeg (for real-world use)
Audio Processing	Basic PCM data handling, normalization, FFT (Fast Fourier Transform)
Speech Recognition	Phonetic models, basic pattern matching
Text Output	Write to file, print to console

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This is a manual approach to audio extraction and speech-to-text, so you'll be building a very basic prototype at first. If the project proves too challenging without libraries, you might consider lightweight libraries or APIs like FFmpeg for audio extraction and CMU Sphinx for speech recognition, which are open-source and lightweight alternatives.

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Would you like to begin with Module 1 on how to extract audio from a video, or start working on any other specific module in more detail?

module-1.md

Absolutely! Let’s dive deeper into Module 1: Video to Audio Extraction, focusing on the process of extracting audio from a video file, and how we can do this step-by-step.

Module 1: Video to Audio Extraction

Goal: Extract the audio stream from a video file and save it in a usable format (e.g., .wav, .mp3).

High-Level Approach:

1. Understand the Video File Format:

   • Video files (e.g., .mp4, .avi, .mkv) are container formats that can hold both video and audio streams, along with other data like subtitles, metadata, and more.
   • The audio track inside the video file can be encoded in different formats like MP3, AAC, AC3, etc.
   • Container formats (like .mp4, .avi, etc.) are just wrappers that store these encoded audio and video streams in their own tracks (also called streams).

2. Extracting the Audio: To extract the audio, we need to:

   • Parse the container file: Understand the structure of the video file (MP4, AVI, MKV, etc.).
   • Identify the audio stream: Every video file has different streams. We need to identify the one containing the audio.
   • Decode the audio: Video containers typically store audio in compressed formats like AAC or MP3. We need to decode this compressed audio to extract the raw PCM audio data.
   • Save the extracted audio: Once we have the decoded audio, we save it as a raw audio file, e.g., .wav or .mp3.

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Steps to Extract Audio:

1. Open and Parse the Video File Header

• Goal: Open the video file and read its metadata to determine how the streams (audio, video) are stored in the container.

  Container Format Overview:

  • MP4: An MP4 file consists of several "atoms" or "boxes," each holding a different part of the file (e.g., video track, audio track, etc.).
  • AVI: An AVI file has a header followed by a list of streams (audio/video). The structure is somewhat simpler than MP4.

  Task: Open the file in binary mode and read its header. You’ll typically need to:

  • Identify file signature/format (e.g., RIFF for AVI, ftyp for MP4).
  • Read the metadata of the file to understand where the streams are located.
  • Extract stream information: There will be separate sections for video and audio streams, so you'll need to extract the audio stream’s index and format.

2. Identify the Audio Stream

• Goal: Parse the video file to identify which stream holds the audio data.

  How to Identify Audio Streams:

  • In formats like MP4, audio is often encoded as AAC or MP3 and placed in specific containers (e.g., mdat box in MP4).

  • AVI files also store audio in separate "chunks," identified by tags (e.g., WAVE).

  • The header information will give you pointers to each stream, whether it’s audio or video. For example:

    • In MP4, there’s a track box for each stream (audio, video, etc.), and you'll need to check the track type (video or audio).
    • In AVI, streams are identified by type IDs (like 0x01 for video and 0x02 for audio).

  Task:

  • Parse the file header and locate the audio stream.
  • Track down the encoding format (e.g., AAC, MP3, PCM) and the sampling rate (e.g., 44.1kHz, 48kHz).

3. Extract and Decode the Audio Stream

• Goal: After identifying the audio stream, the next step is to extract it and decode it into a usable format.

  Decoding the Audio:

  • Audio Compression: Video containers typically store audio in compressed formats (e.g., AAC, MP3). We need to decode these into a raw PCM format, which is uncompressed.

    • For example, AAC or MP3 audio needs to be converted to PCM (Pulse Code Modulation), which is typically stored in formats like .wav.

  • Decoding Process:

    • For compressed formats like MP3 or AAC, you need to use a decoder. The decoder takes compressed audio data and transforms it into PCM, a raw format that represents audio samples.
    • For PCM-encoded audio (e.g., audio in .wav), you can simply copy the data as-is because it’s already in a usable form.

  Task:

  • Extract the raw audio packets from the identified stream.

  • If the audio is compressed (e.g., MP3, AAC), use the appropriate decoding algorithm to convert the audio to PCM.

    • AAC: Use AAC decoding to convert it to PCM.
    • MP3: Use an MP3 decoder to convert it to PCM.

  • For this task, you would typically rely on external libraries (like FFmpeg or libav), as manually decoding these formats is complex.

4. Save the Extracted Audio as a Simple Format (e.g., .wav)

• Goal: Save the decoded PCM audio as a raw audio file (e.g., .wav or .mp3).

  Task:

  • WAV Format: Save the decoded PCM data as a WAV file. WAV files are simple containers that store uncompressed audio in a header (which includes sample rate, number of channels, and bit depth) followed by raw PCM data.
  • MP3 Format: If you want to save it as MP3, you would need to use an MP3 encoder (which is typically an external library like LAME).

  WAV Format:

  • Header: The WAV file starts with a header containing basic information like sample rate (44.1kHz), channels (mono or stereo), and bit depth (e.g., 16-bit). After the header, the raw audio data is stored.
  • Saving the Data: After decoding the audio, you can write the PCM data to a file, properly formatting the WAV header.

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Considerations for Extraction Process

1. File Parsing:

   • Video files are complex, and parsing them manually (without libraries) can be challenging. You’ll need to understand the structure of the format (MP4, AVI, etc.), which could include nested boxes or chunks of data.
   • Each format has its own structure, so you need to handle MP4, AVI, and possibly other formats (MKV, MOV, etc.) separately.

2. Decoding Audio:

   • Audio streams in containers like MP4 or AVI are typically compressed (e.g., MP3, AAC, AC3). Decoding these streams into PCM (uncompressed audio) requires understanding how these formats work.
   • To do this effectively, it’s recommended to use existing decoders (e.g., FFmpeg) because building a custom decoder for each format is non-trivial.

3. Choosing the Output Format:

   • WAV is a great choice for simplicity and ease because it stores raw audio.
   • MP3 is another option but requires an MP3 encoder to compress the raw audio back into a smaller size.

4. Performance Considerations:

   • Audio extraction can take time, depending on the video length and the efficiency of the decoding process.
   • The compression format (e.g., AAC, MP3) may impact the quality and speed of decoding.

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How to Achieve This Without Libraries (Challenges)

Without using libraries like FFmpeg or libav:

• Manual Parsing: You would need to manually parse the container format (e.g., MP4, AVI) to locate the audio stream, which is a complex task.
• Decoding Audio: You would also have to implement the decoding of audio formats like AAC or MP3 yourself, which is challenging without using any existing decoder (this would involve a lot of signal processing and understanding of codec-specific details).
• Writing WAV: Writing the WAV format is simpler since it's just raw PCM data and a header, but you'd still need to handle the header and audio data structure carefully.

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Conclusion

In summary:

1. Parse the video container (MP4, AVI) to identify the audio stream.
2. Extract and decode the audio stream into raw PCM data.
3. Save the audio as a simple format (like WAV).

While doing this manually without libraries is possible, it’s a highly complex task that requires understanding of container formats, audio codecs, and the raw decoding process. In practice, you would likely use FFmpeg or another library to handle the heavy lifting of parsing video containers and decoding audio streams efficiently.

If you want to avoid libraries for learning purposes, I'd suggest starting with simpler formats (e.g., WAV, uncompressed audio

text-to-speech.md

🔹 PART 1: TEXT PROCESSING / NORMALIZATION (eSpeak NG)

🧠 What it is:

This step prepares raw input text (like what a human might type) and makes it suitable for speech synthesis.

In eSpeak NG, this involves:

• Expanding abbreviations (e.g., Dr. → Doctor)
• Converting numbers (e.g., 123 → one hundred twenty-three)
• Handling punctuation and casing
• Tokenizing text into words and symbols

✅ Implemented using:

• C code (speech.cpp, numbers.cpp)
• Language-specific files (en_dict, en_rules)
• Rule files for custom token/word handling

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🔹 PART 2: PHONEME TO SOUND MAPPING (GRAPHEME-TO-PHONEME)

🧠 What it is:

Converts normalized text into phonemes — the basic sound units of speech.

In eSpeak NG, this uses:

• ✅ A dictionary (lexicon) for known words
• ✅ A rule-based system for unknown or out-of-vocabulary (OOV) words

🛠️ Dictionary lookup:

• Uses compiled binary lexicons (e.g., dict files)

🛠️ Rule-based conversion:

• Uses language-specific G2P rules (e.g., phrules, phontab, rules)
• These define letter-to-phoneme patterns (e.g., ph → f)

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🔹 PART 3: SPEECH SYNTHESIS (WAVEFORM GENERATION)

🧠 What it is:

Generates audio waveforms from the phoneme sequence.

In eSpeak NG, this uses:

• ✅ Formant synthesis (Klatt-style)
  • Simulates vocal tract behavior using signal processing
  • No audio recordings required

🛠️ Implemented using:

• C code (synthesize.cpp, synthdata.cpp)
• Phoneme tables with formant and prosody data (phondata, phontab)

🔊 Output:

• Real-time speech playback (e.g., via ALSA)
• Or save to audio files (WAV)

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✅ Summary: How eSpeak NG Works

TTS Stage	Method Used in eSpeak NG
Text Normalization	Rule-based tokenization + C code
Grapheme-to-Phoneme	Dictionary + Rule-based rules
Speech Synthesis	Formant synthesis (Klatt model)

vid2text.md

Got it! Let's focus on the core functionality with a minimal and clean approach, making a CLI tool that:

• Accepts a video file as input.
• Extracts the audio.
• Converts the audio to text (basic transcription).
• Outputs the transcribed text.

Suggested Name for the Tool:

Vid2Text Simple, catchy, and descriptive — Vid for video, 2 for conversion, and Text for the output.

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Core Modules for Vid2Text

1. Module 1: Video to Audio Extraction

Goal: Extract audio from a video file.

• Input: A video file (e.g., .mp4, .avi).
• Output: A raw audio file (e.g., .wav, .mp3).

Minimal Steps:

• Open and parse the video file header.
• Extract the audio stream (typically from a container like .mp4 or .avi).
• Save the extracted audio to a simple format (e.g., .wav).

Note: If you want to handle this without libraries, you'll need to manually parse container formats, but this is highly complex in C++ without existing libraries like FFmpeg. In practice, even without libraries, using FFmpeg to extract audio is the best way to avoid reinventing the wheel.

CLI Command Example:

vid2text input_video.mp4

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2. Module 2: Audio Preprocessing

Goal: Convert audio into a format suitable for speech-to-text.

• Input: Extracted audio file (e.g., .wav).
• Output: Preprocessed audio (e.g., volume normalization, mono conversion, and noise filtering).

Minimal Steps:

• Read raw audio data (handle simple formats like .wav).
• Convert stereo to mono (if needed).
• Normalize audio volume.
• Save the processed audio back to a .wav file.

CLI Command Example:

vid2text input_video.mp4 --preprocess

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3. Module 3: Speech-to-Text Conversion

Goal: Convert speech from the processed audio to text.

• Input: Preprocessed audio file (e.g., .wav).
• Output: Transcribed text.

Minimal Steps:

• Basic Speech Recognition:

  • You could manually match audio features (like phonemes) to text.
  • Or, at the very least, make a basic transcription system by splitting the audio into segments and using simple pattern recognition (though this is typically low-accuracy without trained models).

In practice, you would likely use an external library like CMU Sphinx or integrate with a service like Google Speech-to-Text for real-world usage. Without external libraries, you'd need to implement an algorithm for phonetic recognition, but this is complex without advanced machine learning models.

For simplicity, here we'll assume that you want to implement just the basic structure.

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4. Module 4: Output Transcribed Text

Goal: Return or save the transcribed text.

• Input: Transcribed text from speech.
• Output: Display or save the text.

Minimal Steps:

• Display the transcribed text in the CLI.
• Optionally save the output to a .txt file for later use.

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Minimal CLI Tool Structure

1. Command-line Tool:

   • Accepts a video file as input.
   • Outputs the transcribed text to the console or a .txt file.

Example Command:

vid2text input_video.mp4 --output output.txt

2. Input/Output:

   • input_video.mp4: Input video file.
   • output.txt: Optional output text file with transcribed text.

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Simplified High-Level Workflow

1. Extract Audio from Video:

   • Parse video file, extract audio, and save it as .wav or .mp3.

2. Preprocess Audio:

   • Normalize the audio.
   • Convert stereo to mono (if necessary).
   • Filter noise and save it for transcription.

3. Speech-to-Text:

   • Convert processed audio to text.

4. Output Text:

   • Display transcribed text in the CLI or save it to a file.

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Simplified Code Flow (Pseudocode)

int main(int argc, char* argv[]) {
    // 1. Parse command-line arguments (video file, output options)
    std::string videoFile = argv[1];
    std::string outputFile = argc > 2 ? argv[2] : "output.txt";

    // 2. Extract audio from video (pseudo-code - requires library support or manual implementation)
    std::string audioFile = extractAudioFromVideo(videoFile);

    // 3. Preprocess audio (normalize, mono conversion, etc.)
    preprocessAudio(audioFile);

    // 4. Perform speech-to-text (either with a simple model or library)
    std::string transcribedText = performSpeechToText(audioFile);

    // 5. Output result (to file or console)
    std::ofstream output(outputFile);
    output << transcribedText;
    output.close();
    
    return 0;
}

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Real-World Consideration

• For actual implementation, FFmpeg is often used to extract audio from video files because of its ability to handle a variety of formats.
• Speech-to-text without libraries is difficult — using a trained model or service is often the easiest way.

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Let me know if you'd like help with any specific part, such as audio extraction or speech-to-text processing, or if you'd like suggestions for using libraries for real-world functionality!