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View Code? Open in Web Editor NEW[ICASSP 2024] This is the official code for "VoiceFlow: Efficient Text-to-Speech with Rectified Flow Matching"
Home Page: https://cantabile-kwok.github.io/VoiceFlow/
[ICASSP 2024] This is the official code for "VoiceFlow: Efficient Text-to-Speech with Rectified Flow Matching"
Home Page: https://cantabile-kwok.github.io/VoiceFlow/
How can i get it ?
$bash extract_fbank.sh --stage 0 --stop_stage 2 --nj 16 --sampling_rate 22050
utils/apply-cmvn.py: FileNotFoundError: [Errno 2] No such file or directory: **/cmvn.ark
Hi author, I attempt to train VoiceFlow in aishell3 dataset, but some noise appeared in synthesized audio.
Maybe it because of english vocoder?
Hi author, I wonder how many epochs can got some good results, which can distinguish human voices
Hi, thanks for your great work. I notice that you add a small gaussian noise when sampling x_t
in CFM
:
mu_t = t_unsqueeze * x1 + (1 - t_unsqueeze) * x0 # conditional Gaussian mean
sigma_t = self.sigma_min
x = mu_t + sigma_t * torch.randn_like(x1) # sample p_t(x|x_0, x_1)
This matches the description in your paper. However, I see most other works of rectified flow do not use this sigma_t
and they simply use the mean value mu_t
as the sampled x_t
. I wonder if you have explored on how big influence this sigma_t
has on the model performance, and what is the appropriate range of values for sigma_t
? Thanks for your help in advance.
Hello,
First of all, thank you for sharing the code!
I was wondering if you have any plans to release a pre-trained model trained on LibriTTS.
This code does not work.
diffnet = DiffSingerNet()
x = torch.randn(2, 80, 10)
x_mask = torch.ones(2, 1, 10)
t = torch.tensor([1])
mu = torch.randn(2, 80, 10)
diffnet(x, x_mask, mu, t)
The following code works.
diffnet = DiffSingerNet()
x = torch.randn(2, 80, 10)
x_mask = torch.ones(2, 1, 10)
t = torch.tensor([1])
mu = torch.randn(2, 128, 10)
diffnet(x, x_mask, mu, t)
"cd model/monotonic_align
python setup.py build_ext --inplace"
wonder which gcc version when compile this code
Is it possible to calculate density like normalising flows and then use it as KL divergence (like vits) for end2end training?
(i saw the easter eggs, just wanna know your thoughts about this)
Thank you for your interesting and valuable research.
I'm having trouble running the following command in terminal:
bash extract_fbank.sh --stage 0 --stop_stage 2 --nj 16
The sampling rate of the original ljspepech dataset is 22050Hz, but an error seems to have occurred in the process of downsampling it to 16kHz.
This is the error message written in 'exp/make_fbank/ljspeech/train/make_fbank_train.*.log'.
`Traceback (most recent call last):
File "path/to/VoiceFlow-TTS/utils/compute-fbank-feats.py", line 105, in <module>
main()
File "/path/to/VoiceFlow-TTS/utils/compute-fbank-feats.py", line 86, in main
assert rate == args.fs
AssertionError
# Accounting: time=2 threads=1
# Ended (code 1) at Tue 09 Apr 2024 02:01:15 AM UTC, elapsed time 2 seconds`
Thank you.
Hello! I'm working on building my own dataset. Could you give me an example of Python code that generates phoneme sequences (text) and their corresponding durations (phn_duration).
How can i get the parse_options.sh ?
$bash extract fbank.sh-stage 0--stop stage 2 --nj 16
extract fbank.sh: line 20:parse options.sh: No such file or directory
Hello,
I think you missed this text file. Would you provide it?
Thank you for your work and sharing!
It seems VoiceFLow-TTS and MATCHA-TTS(https://github.com/shivammehta25/Matcha-TTS/) are very similar?
What is the main diffences between two methods?
And How about the performace on voice quality, for example prosody, and the inference speed?
Best
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