Thanks for your great work on GradTTS! However I recently found a tiny error in the arxiv version 2 of GradTTS paper (https://arxiv.org/pdf/2105.06337.pdf). In Eq.31 and Eq.32 in the appendix, the $X_t$ and $\mu$ are put in the wrong order, i.e. it probably should be $\mu - X_t$ rather than $X_t - \mu$. This typo could be originated from the line above Eq.31, " In our case $f(X_t, t) = \frac 12 \Sigma^{-1}(X_t −\mu)\beta$ and ...", where it should be $f(X_t, t)= \frac 12 \Sigma^{-1}(\mu-X_t)\beta$ instead. The other parts of the paper seem not to be affected by this, and the derivations are solid and fluent. Again, great thanks for the work!

Hi, Thank you for the great work and for releasing the pretrained model. I tried to train the grad-tts model using libritts (multispeaker) and using ljspeech (single speaker) and found that the single-speaker setting gives much better quality than the multispeaker. This is even true when using your released grad-tts-libri-tts.pt. Are you able to get better quality in multispeaker setting? These are a few samples I generated in multispeaker setting using your released model: https://drive.google.com/drive/folders/1ze0_rJXtmPY3JNAwnr0A_9C4OVvULEj7?usp=sharing.

Hi, I am modifying the code for my own purposes. I notice here: https://github.com/huawei-noah/Speech-Backbones/blob/b82fdd546d9d977573c8557f242b06a0770ece8e/Grad-TTS/params.py#L53 the argument is hard-coded, and I guess 22050 and 256 to be the sampling rate and frame shift in the case of LJspeech, right? If this is true, should I change to another value if I am dealing with different datasets?

Thank you for the releasing original implementation of Grad-TTS. I would like to know if a multispeaker setting is available or planned for release.

I am implementing a multispeaker setting using this repo. Will the maintainer of this repo be interested in discussing or providing feedback on multispeaker Grad-TTS implementation?

Regards Ajinkya

[Errno 13] Permission denied: '/home/user/app/Grad-TTS/model/monotonic_align/core.c' Traceback (most recent call last): File "/home/user/.local/lib/python3.8/site-packages/Cython/Build/Dependencies.py", line 1208, in cythonize_one result = compile_single(pyx_file, options, full_module_name=full_module_name) File "/home/user/.local/lib/python3.8/site-packages/Cython/Compiler/Main.py", line 727, in compile_single return run_pipeline(source, options, full_module_name) File "/home/user/.local/lib/python3.8/site-packages/Cython/Compiler/Main.py", line 515, in run_pipeline err, enddata = Pipeline.run_pipeline(pipeline, source) File "/home/user/.local/lib/python3.8/site-packages/Cython/Compiler/Pipeline.py", line 355, in run_pipeline data = run(phase, data) File "/home/user/.local/lib/python3.8/site-packages/Cython/Compiler/Pipeline.py", line 335, in run return phase(data) File "/home/user/.local/lib/python3.8/site-packages/Cython/Compiler/Pipeline.py", line 52, in generate_pyx_code_stage module_node.process_implementation(options, result) File "/home/user/.local/lib/python3.8/site-packages/Cython/Compiler/ModuleNode.py", line 143, in process_implementation self.generate_c_code(env, options, result) File "/home/user/.local/lib/python3.8/site-packages/Cython/Compiler/ModuleNode.py", line 411, in generate_c_code f = open_new_file(result.c_file) File "/home/user/.local/lib/python3.8/site-packages/Cython/Utils.py", line 76, in open_new_file return codecs.open(path, "w", encoding="ISO-8859-1") File "/usr/local/lib/python3.8/codecs.py", line 905, in open file = builtins.open(filename, mode, buffering)

Hi, thanks for sharing the code. I have a folder with wav files of different speakers. I don't understand what to do next to get the trained model. What type of files should be in the "mels" and "embeds" folders. How exactly to fill them. Maybe there is some more detailed instructions?

Hello, I adapted the diffvc code on Mandarin datasets. However, the audio after VC has the problem of tone sandhi. I want to ask the performance is normal ?

i tried the pre-trained model--bneSeq2seqMoL-vctk-libritts460-oneshot provided in bneSeq2seqMoL-vctk-libritts460-oneshot.Then converted source wavs to target wavs in provided demo by your paper .Yours in paper performed better than the model trained in the original paper.Why? have u trained the hifi-gan model again?Thank u!

Great work! I've been studying the paper and the code recently and there's something that confuses me much.

In my understanding, the encoder outputs some Gaussian distributions with different mu for each phoneme, and the DPM decoder recovers mel-spec y from these Gaussians. Hence y is not Gaussian anymore. But I speculate from Eq.(14) and the code that when you are calculating the prior loss, you are actually calculating the log-likelihood of y in the Gaussian distribution of mu. Also, when applying MAS for duration modeling, you also perform the similar kind of likelihood computation to get the soft alignment (which is denoted as log_prior in the code). So I wonder why is it reasonable? I also compared the code of GlowTTS. They use z to evaluate the Gaussian likelihood with mean mu, and z is the transformed latent variable from mel-spec using normalizing flow. That seems more reasonable to me by now, as z is Gaussian by itself.

While loading the pretrained weights of the ST2VecEncoder, I had to replace **conv_cfg_i with **conv_cfg_i.__dict__ in __init__ of the Projector class (SPIRAL/nemo/collections/asr/parts/spec2vec.py). Doing this allowed me to load all the weights and match the keys successfully -- nonetheless, i was curious to know if I was missing any installation?