Streaming inference for XTTS 🚀

TTS/.models.json

@@ -5,12 +5,12 @@
  "xtts_v1": {
  "description": "XTTS-v1 by Coqui with 13 languages and cross-language voice cloning.",
  "hf_url": [
- "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/model.pth",
- "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/config.json",
- "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/vocab.json"
+ "https://huggingface.co/coqui/XTTS-v1/resolve/hifigan/model.pth",
+ "https://huggingface.co/coqui/XTTS-v1/resolve/hifigan/config.json",
+ "https://huggingface.co/coqui/XTTS-v1/resolve/hifigan/vocab.json"
  ],
  "default_vocoder": null,
- "commit": "e9a1953e",
+ "commit": "e5140314",
  "license": "CPML",
  "contact": "info@coqui.ai",
  "tos_required": true

TTS/tts/layers/xtts/gpt.py

@@ -172,7 +172,7 @@ def get_grad_norm_parameter_groups(self):
  "heads": list(self.text_head.parameters()) + list(self.mel_head.parameters()),
  }
 
- def init_gpt_for_inference(self, kv_cache=True):
+ def init_gpt_for_inference(self, kv_cache=True, use_deepspeed=False):
  seq_length = self.max_prompt_tokens + self.max_mel_tokens + self.max_text_tokens + 1
  gpt_config = GPT2Config(
  vocab_size=self.max_mel_tokens,
@@ -195,6 +195,17 @@ def init_gpt_for_inference(self, kv_cache=True):
  )
  self.gpt.wte = self.mel_embedding
 
+ if use_deepspeed:
+ import deepspeed
+ self.ds_engine = deepspeed.init_inference(
+ model=self.gpt_inference.half(), # Transformers models
+ mp_size=1, # Number of GPU
+ dtype=torch.float32, # desired data type of output
+ replace_method="auto", # Lets DS autmatically identify the layer to replace
+ replace_with_kernel_inject=True, # replace the model with the kernel injector
+ )
+ self.gpt_inference = self.ds_engine.module.eval()
+
  def set_inputs_and_targets(self, input, start_token, stop_token):
  inp = F.pad(input, (1, 0), value=start_token)
  tar = F.pad(input, (0, 1), value=stop_token)
@@ -543,3 +554,14 @@ def generate(
  if "return_dict_in_generate" in hf_generate_kwargs:
  return gen.sequences[:, gpt_inputs.shape[1] :], gen
  return gen[:, gpt_inputs.shape[1] :]
+
+ def get_generator(self, fake_inputs, **hf_generate_kwargs):
+ return self.gpt_inference.generate_stream(
+ fake_inputs,
+ bos_token_id=self.start_audio_token,
+ pad_token_id=self.stop_audio_token,
+ eos_token_id=self.stop_audio_token,
+ max_length=self.max_mel_tokens * 2 + self.max_prompt_tokens + self.max_text_tokens,
+ do_stream=True,
+ **hf_generate_kwargs,
+ )