Add hebtts and lothm scripts for TTS model inference setup
thewh1teagle
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687792c
Add vocalized mock configuration and metadata preparation script
thewh1teagle
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51fe840
- Created a new JSON configuration file for vocalized mock phonemes mapping.
- Implemented a Python script to prepare metadata from the RAN speech dataset, including diacritic handling and phonemization using the Phonikud model.
- Added functionality to save various metadata CSV files and character sets.
- Introduced a new `pyproject.toml` file for project metadata and dependencies.
- Created a lock file for the project to manage dependencies.
Update axis labels in WER plot for clarity and improved readability
thewh1teagle
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180f090
Update WER plot with improved model metrics and visualization enhancements
thewh1teagle
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475a735
add all metrics
thewh1teagle
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6c7f537
Add text normalization and update report structure in transcription process
thewh1teagle
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bc8ad9c
Add rtf.json with initial performance metrics for models
thewh1teagle
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429b270
Add unvocalized sentences CSV and update model references in scripts
thewh1teagle
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ab52a44
add lothm
thewh1teagle
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d6383e7
add hebtts samples
thewh1teagle
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c812a22
update wavs
thewh1teagle
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4b11b1a
Add study_unvocalized_non_punc.csv with initial content
thewh1teagle
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c69aca6
Update file paths for CSV and model in Piper TTS script
thewh1teagle
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74029f0
- Changed CSV path to point to the new dataset location:
/Users/user/Documents/audio/phonikud-experiments/comparison/sentences/saspeech_male_phonikud.csv
- Updated model path to use the new ONNX model:
/Users/user/Documents/audio/phonikud-experiments/comparison/model (4).onnx
add saspeech samples
thewh1teagle
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b7715c8
add stts2, sort tables
thewh1teagle
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26bbc8b
Update .gitignore, add piper.py for audio generation, and enhance todo list
thewh1teagle
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fdd064f
add wav files
thewh1teagle
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949d638
Remove mms.py script and associated functionality
thewh1teagle
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71418f1
add wav files
thewh1teagle
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4fbbe4a
add mms files
thewh1teagle
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eb586bd
Add WER transcription script with audio processing and metrics calculation
thewh1teagle
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17d4b8f
- Implemented a script to transcribe audio files using a Whisper model.
- Added functions for downloading the model, preparing audio, and transcribing.
- Integrated WER and CER metrics for evaluating transcription accuracy.
- Generated JSON reports for each audio folder containing mean and individual metrics.
- Utilized pandas for data handling and tqdm for progress tracking.