Comments (3)
cschaefer26
commented on June 30, 2024
1
Hi, the output is a numpy array for each sample with length T containing the duration for each phoneme p_t, which is the number of mel frames for that phoneme. The phonemes for each sample are stored in the text_dict after preprocessing. Do you have an ASR ready for extracting the durations? This is something I will look into soon.
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abylouw
commented on June 30, 2024
Thank you for the prompt reply. Yes, I have htk based ASR models that I can use for my languages. I will report back once I have made some progress.
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cschaefer26
commented on June 30, 2024
Cool, keep me posted! Imo there is potential for quality gains with such an approach.
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Related Issues (20)
- symbols.py for Arabic letters
- Feature request: model compatible to export into onnx
- implement hifigan vocoder?
- Adding pauses to the input text HOT 2
- confuse about duration extract HOT 10
- preprocess.py issues - RAM usage close to 100% but CPU usage is nonexistant HOT 16
- ValueError not enough values to unpack (expected 2 got 0) HOT 2
- making the system available for use with assistive technologies on windows HOT 1
- Bad Alignment HOT 1
- ValueError: need at least one array to stack train_tacotron.py line 192 HOT 1
- Facing problem at preprocessing
- Need instructions for fine tunning
- Problems with attention for dataset consisting of longer samples
- how to train a dataset using a pre-trained model?
- preprocess.py misuses Espeak backend, resulting in slow performance and memory leak HOT 2
- preprocess.py: list index out of range HOT 5
- Multispeaker and new neural voice creation HOT 12
- Non-Latin alphabets
- Bad Attention!
- Training a model twice using a different dataset
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