updated help content
This commit is contained in:
erew123 authored and GitHub committed
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@@ -137,6 +137,17 @@ class AllTalkHelpContent:
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flex: 1 1 50% !important;
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min-width: 0 !important; /* Prevents flex items from overflowing */
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}
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.small-file-upload {
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min-height: 200px;
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max-height: 200px;
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}
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.small-file-upload2 {
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min-height: 90px;
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max-height: 90px;
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}
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"""
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WELCOME = """
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@@ -2520,3 +2531,446 @@ class AllTalkHelpContent:
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Note: The Gradio interface will not reflect these changes until AllTalk is reloaded, as Gradio caches the list.
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"""
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TRANSCRIBE = """
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## 🎯 Transcribe Help
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Transcribe is a tool that converts spoken audio into text using OpenAI's Whisper speech recognition models. It supports multiple output formats and batch processing capabilities.
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"""
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TRANSCRIBE1 = """
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## 🎤 Input Methods
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### File Upload
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- Supported formats: MP3, WAV, M4A, OGG, FLAC, AAC, WMA, AIFF, ALAC, OPUS
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- Recommended max size: 50MB per file (larger allowed)
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- Multiple files supported
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- Quality tips:
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- Clear audio with minimal background noise
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- Consistent volume levels
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- Good microphone quality
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- Minimal echo or reverb
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## 📝 Output Options
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### Format Selection
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1. **TXT Format**
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- Plain text transcription
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- Simple and clean output
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- Easy to edit and process
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- No timing information
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2. **JSON Format**
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- Complete transcription data
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- Includes timing information
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- Speaker segments
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- Confidence scores
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- Additional metadata
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3. **SRT Format**
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- Standard subtitle format
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- Includes timestamps
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- Compatible with video players
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- Useful for captioning
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### Organization Features
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- Optional prefix naming
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- Add custom prefix to files
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- Helps organize batches
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- Useful for project management
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- Examples: "meeting_", "interview_"
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- Automatic timestamping
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- Each batch uniquely identified
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- Prevents file overwrites
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- Easy chronological sorting
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- Format: YYYYMMDD_HHMMSS
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## 🎚️ Model Selection
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### Available Models
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1. **Tiny**
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- Fastest processing
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- Lowest resource usage
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- Basic accuracy
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- Good for quick tests
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2. **Base**
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- Balanced performance
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- Good for most uses
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- Moderate accuracy
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- Efficient processing
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3. **Small**
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- Better accuracy
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- Slightly slower
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- Good all-rounder
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- Recommended default
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4. **Medium**
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- High accuracy
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- Slower processing
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- Better with accents
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- Good for complex audio
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"""
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TRANSCRIBE2 = """
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## 🎚️ Model Selection cont...
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5. **Large**
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- Best accuracy
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- Slowest processing
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- Most resource intensive
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- Best for critical transcription
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## 💡 Best Practices
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1. **File Preparation**
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- Remove unnecessary silence
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- Ensure clear audio quality
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- Split very long recordings
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- Check file format compatibility
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2. **Model Selection Tips**
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- Start with 'base' model
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- Use 'tiny' for quick tests
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- 'medium'/'large' for accuracy
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- Consider processing time needs
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3. **Batch Processing**
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- Group related files
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- Use meaningful prefixes
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- Monitor available space
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- Check progress indicators
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4. **File Management**
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- Use descriptive prefixes
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- Clean up temporary files
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- Download results promptly
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- Organize by project/date
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## ⚠️ Troubleshooting
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- **File Upload Issues**
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- Check file format
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- Verify file size
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- Try splitting large files
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- Ensure stable connection
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- **Processing Errors**
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- Check audio quality
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- Try different model size
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- Ensure sufficient space
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- Monitor system resources
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- **Output Quality Issues**
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- Try larger model
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- Check input audio quality
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- Consider background noise
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- Verify speech clarity
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## 🔧 System Requirements
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- Modern web browser
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- Sufficient storage space
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- Stable internet connection
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- Adequate processing power
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- More important for larger models
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- GPU recommended for large batches
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## 🗂️ Output Directory Structure
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```
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transcriptions/
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├── uploads/
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│ └── (temporary audio files)
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└── output/
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├── project1_20240122_123456/
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│ ├── project1_audio1.txt
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│ ├── project1_audio1.srt
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│ └── project1_summary.json
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└── meeting_20240122_234567/
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├── meeting_recording1.txt
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├── meeting_recording1.srt
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└── meeting_summary.json
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"""
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WHISPER_LANGUAGES = {
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"en": "English",
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"zh": "Chinese",
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"de": "German",
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"es": "Spanish",
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"ru": "Russian",
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"ko": "Korean",
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"fr": "French",
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"ja": "Japanese",
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"pt": "Portuguese",
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"tr": "Turkish",
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"pl": "Polish",
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"ca": "Catalan",
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"nl": "Dutch",
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"ar": "Arabic",
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"sv": "Swedish",
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"it": "Italian",
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"id": "Indonesian",
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"hi": "Hindi",
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"fi": "Finnish",
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"vi": "Vietnamese",
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"he": "Hebrew",
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"uk": "Ukrainian",
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"el": "Greek",
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"ms": "Malay",
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"cs": "Czech",
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"ro": "Romanian",
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"da": "Danish",
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"hu": "Hungarian",
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"ta": "Tamil",
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"no": "Norwegian",
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"th": "Thai",
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"ur": "Urdu",
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"hr": "Croatian",
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"bg": "Bulgarian",
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"lt": "Lithuanian",
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"la": "Latin",
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"mi": "Maori",
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"ml": "Malayalam",
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"cy": "Welsh",
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"sk": "Slovak",
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"te": "Telugu",
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"fa": "Persian",
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"lv": "Latvian",
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"bn": "Bengali",
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"sr": "Serbian",
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"az": "Azerbaijani",
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"sl": "Slovenian",
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"kn": "Kannada",
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"et": "Estonian",
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"mk": "Macedonian",
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"br": "Breton",
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"eu": "Basque",
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"is": "Icelandic",
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"hy": "Armenian",
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"ne": "Nepali",
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"mn": "Mongolian",
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"bs": "Bosnian",
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"kk": "Kazakh",
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"sq": "Albanian",
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"sw": "Swahili",
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"gl": "Galician",
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"mr": "Marathi",
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"pa": "Punjabi",
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"si": "Sinhala",
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"km": "Khmer",
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"sn": "Shona",
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"yo": "Yoruba",
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"so": "Somali",
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"af": "Afrikaans",
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"oc": "Occitan",
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"ka": "Georgian",
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"be": "Belarusian",
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"tg": "Tajik",
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"sd": "Sindhi",
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"gu": "Gujarati",
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"am": "Amharic",
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"yi": "Yiddish",
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"lo": "Lao",
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"uz": "Uzbek",
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"fo": "Faroese",
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"ht": "Haitian Creole",
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"ps": "Pashto",
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"tk": "Turkmen",
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"nn": "Nynorsk",
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"mt": "Maltese",
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"sa": "Sanskrit",
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"lb": "Luxembourgish",
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"my": "Myanmar",
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"bo": "Tibetan",
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"tl": "Tagalog",
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"mg": "Malagasy",
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"as": "Assamese",
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"tt": "Tatar",
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"haw": "Hawaiian",
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"ln": "Lingala",
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"ha": "Hausa",
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"ba": "Bashkir",
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"jw": "Javanese",
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"su": "Sundanese",
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}
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WHISPER_HELP = """
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## 🎤 Dictate Help
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The Dictate feature enables real-time speech-to-text conversion using OpenAI's Whisper models. This guide explains all available settings and provides recommendations for optimal use.
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"""
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WHISPER_HELP1 = """
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## 📝 Basic Settings
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**Known Bug** After unloading the model you have to **refresh the page** to start trancription again. Believed to be a Gradio bug/issue.
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### Whisper Model Selection
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- **Purpose**: Determines the model size and accuracy used for transcription
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- **Options**:
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* `tiny` (0.5GB VRAM): Fastest, basic accuracy, good for testing
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* `base` (1GB VRAM): Good balance of speed and accuracy
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* `small` (2GB VRAM): Better accuracy, suitable for clear speech
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* `medium` (4GB VRAM): High accuracy, handles accents better
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* `large-v3` (10GB VRAM): Best accuracy, handles complex audio
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* `large-v3-turbo` (10GB VRAM): Fast high-quality processing
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* `turbo` (10GB VRAM): Fastest high-quality option
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**Recommendation**: Start with a `turbo` model for speed, or `base` if VRAM is very limited and `large-v3`for best quality<br>
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**Note**: Ensure your GPU has sufficient VRAM. Running out of VRAM will cause crashes
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### Language Settings
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- **Language**: Primary language for transcription
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- **Source Language**: Original speech language if translating
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- **Translate to English**: Converts source language to English
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- **Recommendation**: Set both to match your speech language unless translation needed
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1. **Language**:
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- Sets the language model that Whisper uses to decode speech into text
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- Used as the OUTPUT language for transcription
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- Controls what language the text will be written in
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- Example: Set to English to get English text output
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2. **Source Language**:
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- Tells Whisper what language to expect in the input audio
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- Helps the model better recognize speech patterns and phonemes
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- Improves accuracy when processing non-English speech
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- Example: Set Source Language to French when speaker is speaking French
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### Language Use Case Examples:
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**Same Language Transcription**: Set both to same language (e.g., English/English for English speech to English text)<br>
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**Translation**: Set Source Language to speaker's language (e.g., French) and Language to desired output language (e.g., English)<br>
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**Best Accuracy**: Always set Source Language to match the actual spoken language, even when translating
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**Note**: If "Translate to English" is enabled, it overrides the Language setting and forces English output.
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## Output Format
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- **Types**:
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* `txt`: Simple text file
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* `srt`: Subtitle format with timestamps
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* `json`: Detailed format with metadata
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- **Location**: Files saved to `transcriptions/live_dictation/`
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- **Naming**: `[prefix]_dictation_[timestamp].[format]`
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## 🎚️ Advanced Settings
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### Audio Processing
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- **Enable ALL Audio Enhancements**:
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* Activates **ALL** audio processing features
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* Use when recording quality needs improvement
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* The checkboxes for other settings will not show checked
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- **Apply Noise Reduction**:
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* Reduces background noise
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* **Recommendation**: Enable for noisy environments
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- **Apply Audio Compression**:
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* Evens out volume levels
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* **Use When**: Speaking volume varies significantly
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- **Apply Bandpass Filter**:
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The Bandpass Filter isolates frequencies within the typical range of human speech, filtering out low-frequency rumbles and high-frequency hiss or noise. This improves transcription quality in challenging environments.<br><br>
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- **∿ Low Frequency (Hz)** *(Range: 50 - 120 Hz)*
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- **Default Value:** 85 Hz
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- **Lower Values (e.g., 50–70 Hz):**
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- Recommended for deeper male voices or audio with a broader vocal range.
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- Useful when preserving low vocal harmonics in clean recordings.
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- **Higher Values (e.g., 100–120 Hz):**
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- Filters out low-frequency background hums (e.g., AC noise, mic handling noise).
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- Ideal for recordings with rumble or low-frequency distortion.<br><br>
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- **∿ High Frequency (Hz)** *(Range: 3000 - 5000 Hz)*
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- **Default Value:** 3800 Hz
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- **Lower Values (e.g., 3000–3500 Hz):**
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- Reduces high-pitched noise, hissing, or squeals in noisy environments.
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- Useful for telephone-quality recordings or audio with limited frequency range.
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- **Higher Values (e.g., 4000–5000 Hz):**
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- Captures more speech detail and overtones, particularly for tonal languages or professionally recorded audio.
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- Suitable for higher-quality recordings where clarity is paramount.
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**Use Bandpass Filter in Noisy Environments:** Helps focus on human speech frequencies while ignoring irrelevant noise.
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**Skip Bandpass for Pristine Audio:** For high-quality recordings, skipping this filter ensures no speech frequencies are lost.
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"""
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WHISPER_HELP2 = """
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### Timing and Organization
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- **Add Timestamps**:
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* Adds time markers to transcription
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* Useful for long recordings
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* Format: `[HH:MM:SS] Text`
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- **Enable Speaker Diarization**:
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* Attempts to identify different speakers
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* Basic implementation - best for clear speaker transitions
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* Format: `Speaker 1: Text`
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- **Silence Threshold** *(Range: 0.001 - 0.02)*
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This setting determines the amplitude below which audio is treated as silence. Adjusting this is essential for balancing noise filtering and capturing soft speech.
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- **Default Value:** 0.008
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- **Use Higher Values (e.g., 0.01–0.02):**
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- Reduces false triggers from faint background noise (e.g., air conditioners, hums).
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- Suitable for noisier environments where background noise needs to be suppressed.
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- **Use Lower Values (e.g., 0.001–0.007):**
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- Helps capture quieter or softer speech.
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- Ideal for high-quality recordings with minimal background noise.
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**Tip:** Monitor the **Audio Levels graph** during preprocessing or testing to ensure speech is detected while avoiding unnecessary noise capture. Fine-tune based on the quietest speaker in your audio.
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### **Additional Notes for Optimization:**
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- **Experiment with Combined Settings:**
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- Pair a **higher Silence Threshold** (e.g., 0.01) with a **narrower Bandpass range** (e.g., 100–3500 Hz) in noisy environments.
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- Use a **lower Silence Threshold** (e.g., 0.005) and a **wider Bandpass range** (e.g., 50–4000 Hz) for high-quality or wide-spectrum recordings.
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- **Adapt Based on Speaker Profiles:**
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- Adjust **Low Frequency** for male vs. female speakers.
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- Raise **High Frequency** for tonal clarity in languages like Mandarin or musical recordings.
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## 📊 Real-Time Feedback
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### Audio Levels Graph
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- Shows audio input strength in real-time
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- **How to Read**:
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* X-axis: Time
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* Y-axis: Audio level
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* **Good levels**: Consistent peaks without flatlines or clipping
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* **Too low**: Flat or minimal movement
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* **Too high**: Constant maximum peaks
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## 🔄 Workflow
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1. **Setup**:
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- Select Whisper model based on your GPU's VRAM
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- Choose output format and optional file name prefix
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- Configure advanced settings if needed
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2. **Recording**:
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- Click "Load Model" to initialize
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- Press "Record" to start transcription
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- Speak clearly at a consistent volume
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- Watch Audio Levels for feedback
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- Press "Stop" to pause/resume
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- Use "Finish & Unload" when completely done
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3. **File Management**:
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- Transcripts auto-save to `transcriptions/live_dictation/`
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- Each session creates new timestamped files
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- Multiple formats saved if selected
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## ⚠️ Important Notes
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- **VRAM Usage**:
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* Monitor GPU memory
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* Close other GPU applications
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* Restart application if performance degrades
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- **Best Practices**:
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* Speak clearly and at consistent volume
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* Position microphone correctly
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* Use Audio Levels graph for feedback
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- **Performance Tips**:
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* Use `large-v3-turbo` for best speed/quality balance
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* Enable audio processing only when needed
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* Refresh page between sessions if issues occur
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"""
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Reference in new issue
Block a user