Distributed dataparallel implementation of WaveRNN training
Open
Nobody has claimed this yet.
contributions welcome
enhancement
help wanted
- Dominant language
- Python
- Stars
- 2.9k
- Forks
- 799
- Avg merge
- 58m
- Merged PRs (30d)
- 3
Description
Some speed up over the original WaveRNN training example.
Here is a working version on gist.
On a 8 GPU machine, it should be 10 times faster with batch size 256.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with examples/pipeline_wavernn/main.py and compare it with the working version in the linked gist. Determine how the distributed data-parallel training should fit the example, then verify that the implementation trains WaveRNN across eight GPUs with the expected speedup.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- audio-video-rtc, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100