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Hi,
I see you have several models for Speech Emotion Recognition (SER).
Would you say Vesper is the best?
I have also noticed you use acted databases for training, in your experience, does l…
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for the 16kHz Codec model: the bitrate is 2kbps;
for the 44.1kHz Codec model: the bitrate is 6.89kbps;
for the 48kHz Codec model: the bitrate is 7.5kbps;
#1、Here is the exps/results.txt
Codec SU…
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Here is the result for [SpeechTokenizer](https://github.com/ZhangXInFD/SpeechTokenizer).
The bit rate is 2kbps, following are the results:
**Results in exps/results.txt**
Codec SUPERB applica…
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Hi,
I ran this code on my system but the following line in ensemble_emotion_training.py
labels = np.append(labels, fn.split('/')[2].split('-')[1].split('.')[0])
Threw the following error:
IndexErr…
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### Description
Implement a pipeline for deep learning model training, finetuning, and testing for audio classification tasks
### Tasks
- [ ] Data loading
- [ ] Training/finetuning
- [ ] Testing
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# 16 kHz 2kbps
## parameter size:
encoder (including quantizer) : 29MB decoder: 40MB
### exps/results.txt
Codec SUPERB application evaluation
Stage 1: Run speech emotion recognition.
Acc: 74.…
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Dear Dude,
I tried your code, but the accuracy is very low, at most 59%, do you know why? My data set is Berlin. And I also found that if i set epoch size 10, it will over fit eventually.
Thanks for…
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Hi, I found that codec_superb_data contains many datasets and does not give the code for data preprocessing, does it mean that I need to resynthesize each dataset separately by myself according to the…
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FileNotFoundError Traceback (most recent call last)
in ()
7 rec = EmotionRecognizer(model=my_model, emotions=['sad', 'neutral', 'happy'], balance=True, verbose=0)
…
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I wanted to have a list of possible tutorials that are useful to both researchers and students. I'm considering having a separate issue for each tutorial or one giant issue here with a task list. At l…