Comments (4)
Deepspeech uses TF as the framework. The test found that it seems to be very slow the first time in sess run, which consumes more than 40 seconds. When sess was run for the second time, deepspeech only took 0.4 seconds to get the result of bz205. Trying to figure out what went wrong the first time.
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DeepSpeech allows us to better generalize to new speech sequences despite having only limited amounts of training data. We did some experiments using MFCC or FBANK features instead but using DeepSpeech provided the best results.
Since the training code is publicly available, you can just experiment with different speech features to see if this suffices your expectations in both, quality and speech.
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@zhangqijun And updates on this? I have the same issue. It only needs to run once in my code so wasting about 40 seconds for a small input is a big issue for me.
from voca.
Deepspeech uses TF as the framework. The test found that it seems to be very slow the first time in sess run, which consumes more than 40 seconds. When sess was run for the second time, deepspeech only took 0.4 seconds to get the result of bz205. Trying to figure out what went wrong the first time.
Anyway, what is bz205 ?
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Related Issues (20)
- Can't find "output_graph.pb" or "gstep_52280.model" in trained model
- Missing files from training data HOT 3
- question about the learning rate decay HOT 1
- Which equipment is needed to collect data and how can I buy it? HOT 3
- Difference between generic FLAME models and VOCA-compatible FLAME models? HOT 10
- is the eye blink useful for FLame 2020 model ? HOT 3
- "video.mp4" only has speech but no images ? HOT 4
- Windows support and installing MPI-IS / mesh HOT 1
- Should --uv_template_fname be the same as --template_fname in run_voca.py?
- can you give pretrained_models?
- Initialization Of Decoder Layer
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- Missing data on subj_seq_to_idx.pkl file HOT 1
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