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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import argparse
import logging
logging.getLogger('matplotlib').setLevel(logging.WARNING)
import os
import torch
from torch.utils.data import DataLoader
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from tqdm import tqdm
from cosyvoice.cli.model import CosyVoiceModel
from cosyvoice.dataset.dataset import Dataset
def get_args():
parser = argparse.ArgumentParser(description='inference with your model')
parser.add_argument('--config', required=True, help='config file')
parser.add_argument('--prompt_data', required=True, help='prompt data file')
parser.add_argument('--prompt_utt2data', required=True, help='prompt data file')
parser.add_argument('--tts_text', required=True, help='tts input file')
parser.add_argument('--llm_model', required=True, help='llm model file')
parser.add_argument('--flow_model', required=True, help='flow model file')
parser.add_argument('--hifigan_model', required=True, help='hifigan model file')
parser.add_argument('--gpu',
type=int,
default=-1,
help='gpu id for this rank, -1 for cpu')
parser.add_argument('--mode',
default='sft',
choices=['sft', 'zero_shot'],
help='inference mode')
parser.add_argument('--result_dir', required=True, help='asr result file')
args = parser.parse_args()
print(args)
return args
def main():
args = get_args()
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s')
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
# Init cosyvoice models from configs
use_cuda = args.gpu >= 0 and torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
with open(args.config, 'r') as f:
configs = load_hyperpyyaml(f)
model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'])
model.load(args.llm_model, args.flow_model, args.hifigan_model)
test_dataset = Dataset(args.prompt_data, data_pipeline=configs['data_pipeline'], mode='inference', shuffle=False, partition=False, tts_file=args.tts_text, prompt_utt2data=args.prompt_utt2data)
test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0)
del configs
os.makedirs(args.result_dir, exist_ok=True)
fn = os.path.join(args.result_dir, 'wav.scp')
f = open(fn, 'w')
with torch.no_grad():
for batch_idx, batch in tqdm(enumerate(test_data_loader)):
utts = batch["utts"]
assert len(utts) == 1, "inference mode only support batchsize 1"
text = batch["text"]
text_token = batch["text_token"].to(device)
text_token_len = batch["text_token_len"].to(device)
tts_text = batch["tts_text"]
tts_index = batch["tts_index"]
tts_text_token = batch["tts_text_token"].to(device)
tts_text_token_len = batch["tts_text_token_len"].to(device)
speech_token = batch["speech_token"].to(device)
speech_token_len = batch["speech_token_len"].to(device)
speech_feat = batch["speech_feat"].to(device)
speech_feat_len = batch["speech_feat_len"].to(device)
utt_embedding = batch["utt_embedding"].to(device)
spk_embedding = batch["spk_embedding"].to(device)
if args.mode == 'sft':
model_input = {'text': tts_text_token, 'text_len': tts_text_token_len,
'llm_embedding': spk_embedding, 'flow_embedding': spk_embedding}
else:
model_input = {'text': tts_text_token, 'text_len': tts_text_token_len,
'prompt_text': text_token, 'prompt_text_len': text_token_len,
'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len,
'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len,
'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len,
'llm_embedding': utt_embedding, 'flow_embedding': utt_embedding}
model_output = model.inference(**model_input)
tts_key = '{}_{}'.format(utts[0], tts_index[0])
tts_fn = os.path.join(args.result_dir, '{}.wav'.format(tts_key))
torchaudio.save(tts_fn, model_output['tts_speech'], sample_rate=22050)
f.write('{} {}\n'.format(tts_key, tts_fn))
f.flush()
f.close()
logging.info('Result wav.scp saved in {}'.format(fn))
if __name__ == '__main__':
main()
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