Comments (2)
I am not sure of the meaning of the sentence "Are your models really output a speech or not?"
The output of my FastSpeech2 implementation is a mel-spectrogram, which can be conversed to wavfiles by vocoders such as WaveGlow and MelGAN.
If you are asking that why there is a large gap between the training and validation mel_loss and mel_postnet_loss curves, it is because that in evaluate.py
the model synthesizes mel-spectrograms without ground-truth F0 and energy labels.
Lines 63 to 64 in 172d2ea
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closed #4
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Related Issues (20)
- fine-tuning issue HOT 1
- Inconvergence in pitch and energy loss
- 求救!!在VarianceAdaptor中 进行pith_embeding的时候显示编码器输出张量x和音素嵌入张量形状不同无法相加
- The numbers of the audio samples and speakers mismatch of LibriTTS dataset
- Duration of synthesis output is very short
- how to slove indexSelectSmallIndex: block: [1,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed when training ASILLE3 HOT 1
- Minor bug in loading vocoders
- not found modules
- ran out of input
- [CONTRIBUTION] Speech Dataset Generator
- MFA version HOT 4
- Discrepancy in the Number of Decoder Layers
- How to train with Indian Accent HOT 2
- data
- FileNotFoundError: [Errno 2] No such file or directory: '/root/FastSpeech2-master/preprocessed_data/LJSpeech/mel/LJSpeech-mel-LJ016-0364.npy'
- preprocess.py
- train/py
- train.yaml
- CUDA out of memory
- amazing work,can it support that generate the phoneme delayed time sequence?
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