updated help content

This commit is contained in:
erew123 authored and GitHub committed 2024-11-22 19:25:32 +00:00
1 parent ddbe382e67
commit 2edfd4f517
1 file changed
+454
+454
View File
@@ -137,6 +137,17 @@ class AllTalkHelpContent:
flex: 1 1 50% !important;
min-width: 0 !important; /* Prevents flex items from overflowing */
}
.small-file-upload {
min-height: 200px;
max-height: 200px;
}
.small-file-upload2 {
min-height: 90px;
max-height: 90px;
}
"""
WELCOME = """
@@ -2520,3 +2531,446 @@ class AllTalkHelpContent:
Note: The Gradio interface will not reflect these changes until AllTalk is reloaded, as Gradio caches the list.
"""
TRANSCRIBE = """
## 🎯 Transcribe Help
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.
"""
TRANSCRIBE1 = """
## 🎤 Input Methods
### File Upload
- Supported formats: MP3, WAV, M4A, OGG, FLAC, AAC, WMA, AIFF, ALAC, OPUS
- Recommended max size: 50MB per file (larger allowed)
- Multiple files supported
- Quality tips:
- Clear audio with minimal background noise
- Consistent volume levels
- Good microphone quality
- Minimal echo or reverb
## 📝 Output Options
### Format Selection
1. **TXT Format**
- Plain text transcription
- Simple and clean output
- Easy to edit and process
- No timing information
2. **JSON Format**
- Complete transcription data
- Includes timing information
- Speaker segments
- Confidence scores
- Additional metadata
3. **SRT Format**
- Standard subtitle format
- Includes timestamps
- Compatible with video players
- Useful for captioning
### Organization Features
- Optional prefix naming
- Add custom prefix to files
- Helps organize batches
- Useful for project management
- Examples: "meeting_", "interview_"
- Automatic timestamping
- Each batch uniquely identified
- Prevents file overwrites
- Easy chronological sorting
- Format: YYYYMMDD_HHMMSS
## 🎚️ Model Selection
### Available Models
1. **Tiny**
- Fastest processing
- Lowest resource usage
- Basic accuracy
- Good for quick tests
2. **Base**
- Balanced performance
- Good for most uses
- Moderate accuracy
- Efficient processing
3. **Small**
- Better accuracy
- Slightly slower
- Good all-rounder
- Recommended default
4. **Medium**
- High accuracy
- Slower processing
- Better with accents
- Good for complex audio
"""
TRANSCRIBE2 = """
## 🎚️ Model Selection cont...
5. **Large**
- Best accuracy
- Slowest processing
- Most resource intensive
- Best for critical transcription
## 💡 Best Practices
1. **File Preparation**
- Remove unnecessary silence
- Ensure clear audio quality
- Split very long recordings
- Check file format compatibility
2. **Model Selection Tips**
- Start with 'base' model
- Use 'tiny' for quick tests
- 'medium'/'large' for accuracy
- Consider processing time needs
3. **Batch Processing**
- Group related files
- Use meaningful prefixes
- Monitor available space
- Check progress indicators
4. **File Management**
- Use descriptive prefixes
- Clean up temporary files
- Download results promptly
- Organize by project/date
## ⚠️ Troubleshooting
- **File Upload Issues**
- Check file format
- Verify file size
- Try splitting large files
- Ensure stable connection
- **Processing Errors**
- Check audio quality
- Try different model size
- Ensure sufficient space
- Monitor system resources
- **Output Quality Issues**
- Try larger model
- Check input audio quality
- Consider background noise
- Verify speech clarity
## 🔧 System Requirements
- Modern web browser
- Sufficient storage space
- Stable internet connection
- Adequate processing power
- More important for larger models
- GPU recommended for large batches
## 🗂️ Output Directory Structure
```
transcriptions/
├── uploads/
│ └── (temporary audio files)
└── output/
├── project1_20240122_123456/
│ ├── project1_audio1.txt
│ ├── project1_audio1.srt
│ └── project1_summary.json
└── meeting_20240122_234567/
├── meeting_recording1.txt
├── meeting_recording1.srt
└── meeting_summary.json
"""
WHISPER_LANGUAGES = {
"en": "English",
"zh": "Chinese",
"de": "German",
"es": "Spanish",
"ru": "Russian",
"ko": "Korean",
"fr": "French",
"ja": "Japanese",
"pt": "Portuguese",
"tr": "Turkish",
"pl": "Polish",
"ca": "Catalan",
"nl": "Dutch",
"ar": "Arabic",
"sv": "Swedish",
"it": "Italian",
"id": "Indonesian",
"hi": "Hindi",
"fi": "Finnish",
"vi": "Vietnamese",
"he": "Hebrew",
"uk": "Ukrainian",
"el": "Greek",
"ms": "Malay",
"cs": "Czech",
"ro": "Romanian",
"da": "Danish",
"hu": "Hungarian",
"ta": "Tamil",
"no": "Norwegian",
"th": "Thai",
"ur": "Urdu",
"hr": "Croatian",
"bg": "Bulgarian",
"lt": "Lithuanian",
"la": "Latin",
"mi": "Maori",
"ml": "Malayalam",
"cy": "Welsh",
"sk": "Slovak",
"te": "Telugu",
"fa": "Persian",
"lv": "Latvian",
"bn": "Bengali",
"sr": "Serbian",
"az": "Azerbaijani",
"sl": "Slovenian",
"kn": "Kannada",
"et": "Estonian",
"mk": "Macedonian",
"br": "Breton",
"eu": "Basque",
"is": "Icelandic",
"hy": "Armenian",
"ne": "Nepali",
"mn": "Mongolian",
"bs": "Bosnian",
"kk": "Kazakh",
"sq": "Albanian",
"sw": "Swahili",
"gl": "Galician",
"mr": "Marathi",
"pa": "Punjabi",
"si": "Sinhala",
"km": "Khmer",
"sn": "Shona",
"yo": "Yoruba",
"so": "Somali",
"af": "Afrikaans",
"oc": "Occitan",
"ka": "Georgian",
"be": "Belarusian",
"tg": "Tajik",
"sd": "Sindhi",
"gu": "Gujarati",
"am": "Amharic",
"yi": "Yiddish",
"lo": "Lao",
"uz": "Uzbek",
"fo": "Faroese",
"ht": "Haitian Creole",
"ps": "Pashto",
"tk": "Turkmen",
"nn": "Nynorsk",
"mt": "Maltese",
"sa": "Sanskrit",
"lb": "Luxembourgish",
"my": "Myanmar",
"bo": "Tibetan",
"tl": "Tagalog",
"mg": "Malagasy",
"as": "Assamese",
"tt": "Tatar",
"haw": "Hawaiian",
"ln": "Lingala",
"ha": "Hausa",
"ba": "Bashkir",
"jw": "Javanese",
"su": "Sundanese",
}
WHISPER_HELP = """
## 🎤 Dictate Help
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.
"""
WHISPER_HELP1 = """
## 📝 Basic Settings
**Known Bug** After unloading the model you have to **refresh the page** to start trancription again. Believed to be a Gradio bug/issue.
### Whisper Model Selection
- **Purpose**: Determines the model size and accuracy used for transcription
- **Options**:
* `tiny` (0.5GB VRAM): Fastest, basic accuracy, good for testing
* `base` (1GB VRAM): Good balance of speed and accuracy
* `small` (2GB VRAM): Better accuracy, suitable for clear speech
* `medium` (4GB VRAM): High accuracy, handles accents better
* `large-v3` (10GB VRAM): Best accuracy, handles complex audio
* `large-v3-turbo` (10GB VRAM): Fast high-quality processing
* `turbo` (10GB VRAM): Fastest high-quality option
**Recommendation**: Start with a `turbo` model for speed, or `base` if VRAM is very limited and `large-v3`for best quality<br>
**Note**: Ensure your GPU has sufficient VRAM. Running out of VRAM will cause crashes
### Language Settings
- **Language**: Primary language for transcription
- **Source Language**: Original speech language if translating
- **Translate to English**: Converts source language to English
- **Recommendation**: Set both to match your speech language unless translation needed
1. **Language**:
- Sets the language model that Whisper uses to decode speech into text
- Used as the OUTPUT language for transcription
- Controls what language the text will be written in
- Example: Set to English to get English text output
2. **Source Language**:
- Tells Whisper what language to expect in the input audio
- Helps the model better recognize speech patterns and phonemes
- Improves accuracy when processing non-English speech
- Example: Set Source Language to French when speaker is speaking French
### Language Use Case Examples:
**Same Language Transcription**: Set both to same language (e.g., English/English for English speech to English text)<br>
**Translation**: Set Source Language to speaker's language (e.g., French) and Language to desired output language (e.g., English)<br>
**Best Accuracy**: Always set Source Language to match the actual spoken language, even when translating
**Note**: If "Translate to English" is enabled, it overrides the Language setting and forces English output.
## Output Format
- **Types**:
* `txt`: Simple text file
* `srt`: Subtitle format with timestamps
* `json`: Detailed format with metadata
- **Location**: Files saved to `transcriptions/live_dictation/`
- **Naming**: `[prefix]_dictation_[timestamp].[format]`
## 🎚️ Advanced Settings
### Audio Processing
- **Enable ALL Audio Enhancements**:
* Activates **ALL** audio processing features
* Use when recording quality needs improvement
* The checkboxes for other settings will not show checked
- **Apply Noise Reduction**:
* Reduces background noise
* **Recommendation**: Enable for noisy environments
- **Apply Audio Compression**:
* Evens out volume levels
* **Use When**: Speaking volume varies significantly
- **Apply Bandpass Filter**:
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>
- **∿ Low Frequency (Hz)** *(Range: 50 - 120 Hz)*
- **Default Value:** 85 Hz
- **Lower Values (e.g., 50–70 Hz):**
- Recommended for deeper male voices or audio with a broader vocal range.
- Useful when preserving low vocal harmonics in clean recordings.
- **Higher Values (e.g., 100–120 Hz):**
- Filters out low-frequency background hums (e.g., AC noise, mic handling noise).
- Ideal for recordings with rumble or low-frequency distortion.<br><br>
- **∿ High Frequency (Hz)** *(Range: 3000 - 5000 Hz)*
- **Default Value:** 3800 Hz
- **Lower Values (e.g., 3000–3500 Hz):**
- Reduces high-pitched noise, hissing, or squeals in noisy environments.
- Useful for telephone-quality recordings or audio with limited frequency range.
- **Higher Values (e.g., 4000–5000 Hz):**
- Captures more speech detail and overtones, particularly for tonal languages or professionally recorded audio.
- Suitable for higher-quality recordings where clarity is paramount.
**Use Bandpass Filter in Noisy Environments:** Helps focus on human speech frequencies while ignoring irrelevant noise.
**Skip Bandpass for Pristine Audio:** For high-quality recordings, skipping this filter ensures no speech frequencies are lost.
"""
WHISPER_HELP2 = """
### Timing and Organization
- **Add Timestamps**:
* Adds time markers to transcription
* Useful for long recordings
* Format: `[HH:MM:SS] Text`
- **Enable Speaker Diarization**:
* Attempts to identify different speakers
* Basic implementation - best for clear speaker transitions
* Format: `Speaker 1: Text`
- **Silence Threshold** *(Range: 0.001 - 0.02)*
This setting determines the amplitude below which audio is treated as silence. Adjusting this is essential for balancing noise filtering and capturing soft speech.
- **Default Value:** 0.008
- **Use Higher Values (e.g., 0.01–0.02):**
- Reduces false triggers from faint background noise (e.g., air conditioners, hums).
- Suitable for noisier environments where background noise needs to be suppressed.
- **Use Lower Values (e.g., 0.001–0.007):**
- Helps capture quieter or softer speech.
- Ideal for high-quality recordings with minimal background noise.
**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.
### **Additional Notes for Optimization:**
- **Experiment with Combined Settings:**
- Pair a **higher Silence Threshold** (e.g., 0.01) with a **narrower Bandpass range** (e.g., 100–3500 Hz) in noisy environments.
- 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.
- **Adapt Based on Speaker Profiles:**
- Adjust **Low Frequency** for male vs. female speakers.
- Raise **High Frequency** for tonal clarity in languages like Mandarin or musical recordings.
## 📊 Real-Time Feedback
### Audio Levels Graph
- Shows audio input strength in real-time
- **How to Read**:
* X-axis: Time
* Y-axis: Audio level
* **Good levels**: Consistent peaks without flatlines or clipping
* **Too low**: Flat or minimal movement
* **Too high**: Constant maximum peaks
## 🔄 Workflow
1. **Setup**:
- Select Whisper model based on your GPU's VRAM
- Choose output format and optional file name prefix
- Configure advanced settings if needed
2. **Recording**:
- Click "Load Model" to initialize
- Press "Record" to start transcription
- Speak clearly at a consistent volume
- Watch Audio Levels for feedback
- Press "Stop" to pause/resume
- Use "Finish & Unload" when completely done
3. **File Management**:
- Transcripts auto-save to `transcriptions/live_dictation/`
- Each session creates new timestamped files
- Multiple formats saved if selected
## ⚠️ Important Notes
- **VRAM Usage**:
* Monitor GPU memory
* Close other GPU applications
* Restart application if performance degrades
- **Best Practices**:
* Speak clearly and at consistent volume
* Position microphone correctly
* Use Audio Levels graph for feedback
- **Performance Tips**:
* Use `large-v3-turbo` for best speed/quality balance
* Enable audio processing only when needed
* Refresh page between sessions if issues occur
"""