Comments (3)
Hi,
No, you could just overwrite the maximum length in the config file. That being said, the model is not really made to estimate such long speech samples.
You can overwrite the config of the checkpoint and write a new checkpoint file with the model weights as follows:
import torch
checkpoint = torch.load('weights/nisqa.tar')
checkpoint['args']['ms_max_segments'] = 3000
torch.save(checkpoint, 'weights/nisqa_2.tar')
Then you can run the model with the new checkpoint:
python run_predict.py --mode predict_file --pretrained_model weights/nisqa_2.tar --deg /path/to/wav/file.wav
Let me know if it works!
from nisqa.
it works! Thank you
from nisqa.
Great!
from nisqa.
Related Issues (20)
- Is it possible to use the model as a function? HOT 1
- Can this model be modified into a speech quality classification model? HOT 1
- RuntimeError: Could not infer dtype of numpy.float32 HOT 10
- Using NISQA as a loss function HOT 2
- NISQA Corpus download issue HOT 1
- Continuous metrics? HOT 4
- TTS naturalness prediction based on which model
- Could you tell me if the MOS rating is objective or subjective? HOT 4
- Full Reference or No Reference When Subjectively Rating a Speech HOT 1
- upper bound and larger bound inconsistent with step sign HOT 1
- Audio input requirements HOT 2
- pip package HOT 4
- CDUA device does not load the model HOT 2
- Interpertation of Different metrics HOT 1
- The predict result seems not reliable HOT 3
- License
- upper bound and larger bound inconsistent with step sign
- It seams slowly because of some functions running on CPU
- max window length error for most audio files HOT 1
- Utilizing finetuned weights
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from nisqa.