Comments (4)
Hi, @lycox1
Thanks for your interest in VoiceFilter open-source repo.
As discussed in #5, SDR may significantly differ from results in README since it's measured from the random sample. Please refer to Jungwon Seo's comment here: #5 (comment)
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Thanks @seungwonpark
I already read #5.
I think that key checkpoint of #5 are below
- train-other-500 don't use for training. Just use train-clean-100 and train-clean-360
--> I use train-clean-100, train-clean-360 and dev-clean - comparing to the published sample. (origianl paper's sample https://google.github.io/speaker-id/publications/VoiceFilter/).
--> I checked the dev_tuples.csv and train_tuples.csv (https://github.com/google/speaker-id/tree/master/publications/VoiceFilter/dataset/LibriSpeech). Files in dev-clean are exist in dev_tuples.csv but files in train-clean-100 and train-clean-360 don't exists in dev_tuples.csv and train_tuples.csv.
Could plz let me know if you have any other clue!
Thanks.
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Hello @seungwonpark , I also get the similar problem with @lycox1 . Could you please give me a hand?
I almost follow all the README steps, except that the suffix of audios in LibriSpeech is .flac, so I changed 24th line of normalize-resample.sh from
"for f in $(find . -name ".wav"); do"
to
"for f in $(find . -name ".flac"); do".
Since I clone down the newest code, train-other-500 has been removed. By the way, I notice that in README the number of test cases is 1000, while the code use only 100 test cases.
Here are the images of the training loss, test loss and test SDR in my experiment. Although the test data may be different, I believe that a correct training loss curve should be similar, right?
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Hi, @lawlict
The test loss curve may fluctuate since we didn't perform the evaluation for a sufficient amount of data. So I think the curve may look bit different.
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Related Issues (20)
- Question about normalize-resample.sh HOT 4
- Question when preprocessing wav files HOT 1
- Question when training VoiceFilter HOT 1
- Question about utils/evaluation.py HOT 2
- Need to try power-law compression loss HOT 2
- embedder.pt with new dataset HOT 4
- Can you get the initial mean SDR on LibriSpeech using Google's test list? HOT 8
- hop_length and win_length
- the model implementation comprehension HOT 1
- inference
- Can I get the pretrained model please! I so dearly need it for my project, here's my email just in case, [email protected] HOT 1
- Training setting problem HOT 6
- Is the VoiceFilter model checkpoint available to be used directly? HOT 1
- question about ffmpeg-normalize
- Question about wav2spec function in utils/audo.py
- Cannot reproduce reported SDR & retrain the speaker embedding
- how to work for multi noise
- What is the term spk refers to in the below code ? line 127
- how to create file embedder HOT 5
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