We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| expand_speaker_embed | Ran | this paper's copy was not recorded; identical code first harvested from r9y9/deepvoice3_pytorch pointer only · get_code("afbe89316d22c0f4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on one single-GPU server.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1710.07654")
get_code_for_paper("1710.07654")
have("1710.07654")
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