mirror of https://github.com/coqui-ai/TTS.git
fix tests
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fede89ac0d
commit
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@ -306,6 +306,7 @@ If you don't specify any models, then it uses LJSpeech based English model.
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encoder_config_path = None
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vc_path = None
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vc_config_path = None
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model_dir = None
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# CASE1 #list : list pre-trained TTS models
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if args.list_models:
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@ -336,6 +337,9 @@ If you don't specify any models, then it uses LJSpeech based English model.
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if args.model_name is not None and not args.model_path:
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model_path, config_path, model_item = manager.download_model(args.model_name)
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# tortoise model
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if model_path.split("--")[-1] == "tortoise-v2":
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model_dir = model_path
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# tts model
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if model_item["model_type"] == "tts_models":
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tts_path = model_path
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@ -379,6 +383,7 @@ If you don't specify any models, then it uses LJSpeech based English model.
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encoder_config_path,
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vc_path,
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vc_config_path,
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model_dir,
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args.use_cuda,
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)
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@ -1,7 +1,7 @@
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import os
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import random
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from contextlib import contextmanager
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from dataclasses import dataclass, field
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from dataclasses import dataclass
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from time import time
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import torch
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@ -392,11 +392,6 @@ class Tortoise(BaseTTS):
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yield m
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m = model.cpu()
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def speaker_manager(
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self,
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):
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self.speaker_names = os.listdir()
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def get_conditioning_latents(
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self,
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voice_samples,
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@ -804,17 +799,17 @@ class Tortoise(BaseTTS):
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}
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return return_dict
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def forward():
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def forward(self):
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raise NotImplementedError("Tortoise Training is not implemented")
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def eval_step():
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def eval_step(self):
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raise NotImplementedError("Tortoise Training is not implemented")
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def init_from_config():
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def init_from_config(self):
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raise NotImplementedError("Tortoise Training is not implemented")
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def load_checkpoint():
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def load_checkpoint(self):
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raise NotImplementedError("Tortoise Training is not implemented")
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def train_step():
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def train_step(self):
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raise NotImplementedError("Tortoise Training is not implemented")
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@ -299,7 +299,6 @@ class ModelManager(object):
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model_file = None
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config_file = None
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for file_name in os.listdir(output_path):
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print(file_name)
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if file_name in ["model_file.pth", "model_file.pth.tar", "model.pth"]:
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model_file = os.path.join(output_path, file_name)
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elif file_name == "config.json":
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