add hifigan train

cosyvoice/bin/average_model.py

@@ -0,0 +1,93 @@
+# Copyright (c) 2020 Mobvoi Inc (Di Wu)
+# 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.
+
+import os
+import argparse
+import glob
+import sys
+
+import yaml
+import torch
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='average model')
+    parser.add_argument('--dst_model', required=True, help='averaged model')
+    parser.add_argument('--src_path',
+                        required=True,
+                        help='src model path for average')
+    parser.add_argument('--val_best',
+                        action="store_true",
+                        help='averaged model')
+    parser.add_argument('--num',
+                        default=5,
+                        type=int,
+                        help='nums for averaged model')
+
+    args = parser.parse_args()
+    print(args)
+    return args
+
+
+def main():
+    args = get_args()
+    val_scores = []
+    if args.val_best:
+        yamls = glob.glob('{}/*.yaml'.format(args.src_path))
+        yamls = [
+            f for f in yamls
+            if not (os.path.basename(f).startswith('train')
+                    or os.path.basename(f).startswith('init'))
+        ]
+        for y in yamls:
+            with open(y, 'r') as f:
+                dic_yaml = yaml.load(f, Loader=yaml.BaseLoader)
+                loss = float(dic_yaml['loss_dict']['loss'])
+                epoch = int(dic_yaml['epoch'])
+                step = int(dic_yaml['step'])
+                tag = dic_yaml['tag']
+                val_scores += [[epoch, step, loss, tag]]
+        sorted_val_scores = sorted(val_scores,
+                                   key=lambda x: x[2],
+                                   reverse=False)
+        print("best val (epoch, step, loss, tag) = " +
+              str(sorted_val_scores[:args.num]))
+        path_list = [
+            args.src_path + '/epoch_{}_whole.pt'.format(score[0])
+            for score in sorted_val_scores[:args.num]
+        ]
+    print(path_list)
+    avg = {}
+    num = args.num
+    assert num == len(path_list)
+    for path in path_list:
+        print('Processing {}'.format(path))
+        states = torch.load(path, map_location=torch.device('cpu'))
+        for k in states.keys():
+            if k not in avg.keys():
+                avg[k] = states[k].clone()
+            else:
+                avg[k] += states[k]
+    # average
+    for k in avg.keys():
+        if avg[k] is not None:
+            # pytorch 1.6 use true_divide instead of /=
+            avg[k] = torch.true_divide(avg[k], num)
+    print('Saving to {}'.format(args.dst_model))
+    torch.save(avg, args.dst_model)
+
+
+if __name__ == '__main__':
+    main()

cosyvoice/bin/train.py

@@ -86,8 +86,12 @@ def main():
     args = get_args()
     logging.basicConfig(level=logging.DEBUG,
                         format='%(asctime)s %(levelname)s %(message)s')
+    # gan train has some special initialization logic
+    gan = True if args.model == 'hifigan' else False
 
-    override_dict = {k: None for k in ['llm', 'flow', 'hifigan'] if k != args.model}
+    override_dict = {k: None for k in ['llm', 'flow', 'hift', 'hifigan'] if k != args.model}
+    if gan is True:
+        override_dict.pop('hift')
     with open(args.config, 'r') as f:
         configs = load_hyperpyyaml(f, overrides=override_dict)
     configs['train_conf'].update(vars(args))
@@ -97,7 +101,7 @@ def main():
 
     # Get dataset & dataloader
     train_dataset, cv_dataset, train_data_loader, cv_data_loader = \
-        init_dataset_and_dataloader(args, configs)
+        init_dataset_and_dataloader(args, configs, gan)
 
     # Do some sanity checks and save config to arsg.model_dir
     configs = check_modify_and_save_config(args, configs)
@@ -108,13 +112,13 @@ def main():
     # load checkpoint
     model = configs[args.model]
     if args.checkpoint is not None:
-        model.load_state_dict(torch.load(args.checkpoint, map_location='cpu'))
+        model.load_state_dict(torch.load(args.checkpoint, map_location='cpu'), strict=False)
 
     # Dispatch model from cpu to gpu
     model = wrap_cuda_model(args, model)
 
     # Get optimizer & scheduler
-    model, optimizer, scheduler = init_optimizer_and_scheduler(args, configs, model)
+    model, optimizer, scheduler, optimizer_d, scheduler_d = init_optimizer_and_scheduler(args, configs, model, gan)
 
     # Save init checkpoints
     info_dict = deepcopy(configs['train_conf'])
@@ -129,7 +133,10 @@ def main():
         train_dataset.set_epoch(epoch)
         dist.barrier()
         group_join = dist.new_group(backend="gloo", timeout=datetime.timedelta(seconds=args.timeout))
-        executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, group_join)
+        if gan is True:
+            executor.train_one_epoc_gan(model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, writer, info_dict, group_join)
+        else:
+            executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, group_join)
         dist.destroy_process_group(group_join)
 
 

cosyvoice/bin/train_gan.py

@@ -1,137 +0,0 @@
-# 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 datetime
-import logging
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-from copy import deepcopy
-import torch
-import torch.distributed as dist
-import deepspeed
-
-from hyperpyyaml import load_hyperpyyaml
-
-from torch.distributed.elastic.multiprocessing.errors import record
-
-from cosyvoice.utils.executor_gan import Executor
-from cosyvoice.utils.train_utils import (
-    init_distributed,
-    init_dataset_and_dataloader,
-    init_optimizer_and_scheduler_gan,
-    init_summarywriter, save_model,
-    wrap_cuda_model, check_modify_and_save_config)
-
-
-def get_args():
-    parser = argparse.ArgumentParser(description='training your network')
-    parser.add_argument('--train_engine',
-                        default='torch_ddp',
-                        choices=['torch_ddp', 'deepspeed'],
-                        help='Engine for paralleled training')
-    parser.add_argument('--model', required=True, help='model which will be trained')
-    parser.add_argument('--config', required=True, help='config file')
-    parser.add_argument('--train_data', required=True, help='train data file')
-    parser.add_argument('--cv_data', required=True, help='cv data file')
-    parser.add_argument('--checkpoint', help='checkpoint model')
-    parser.add_argument('--model_dir', required=True, help='save model dir')
-    parser.add_argument('--tensorboard_dir',
-                        default='tensorboard',
-                        help='tensorboard log dir')
-    parser.add_argument('--ddp.dist_backend',
-                        dest='dist_backend',
-                        default='nccl',
-                        choices=['nccl', 'gloo'],
-                        help='distributed backend')
-    parser.add_argument('--num_workers',
-                        default=0,
-                        type=int,
-                        help='num of subprocess workers for reading')
-    parser.add_argument('--prefetch',
-                        default=100,
-                        type=int,
-                        help='prefetch number')
-    parser.add_argument('--pin_memory',
-                        action='store_true',
-                        default=False,
-                        help='Use pinned memory buffers used for reading')
-    parser.add_argument('--deepspeed.save_states',
-                        dest='save_states',
-                        default='model_only',
-                        choices=['model_only', 'model+optimizer'],
-                        help='save model/optimizer states')
-    parser.add_argument('--timeout',
-                        default=30,
-                        type=int,
-                        help='timeout (in seconds) of cosyvoice_join.')
-    parser = deepspeed.add_config_arguments(parser)
-    args = parser.parse_args()
-    return args
-
-
-@record
-def main():
-    args = get_args()
-    logging.basicConfig(level=logging.DEBUG,
-                        format='%(asctime)s %(levelname)s %(message)s')
-
-    override_dict = {k: None for k in ['llm', 'flow', 'hifigan'] if k != args.model}
-    with open(args.config, 'r') as f:
-        configs = load_hyperpyyaml(f, overrides=override_dict, overrides_must_match=False)
-    configs['train_conf'].update(vars(args))
-
-    # Init env for ddp
-    init_distributed(args)
-
-    # Get dataset & dataloader
-    train_dataset, cv_dataset, train_data_loader, cv_data_loader = \
-        init_dataset_and_dataloader(args, configs)
-
-    # Do some sanity checks and save config to arsg.model_dir
-    configs = check_modify_and_save_config(args, configs)
-
-    # Tensorboard summary
-    writer = init_summarywriter(args)
-
-    # load checkpoint
-    model = configs[args.model]
-    if args.checkpoint is not None:
-        model.load_state_dict(torch.load(args.checkpoint, map_location='cpu'))
-
-    # Dispatch model from cpu to gpu
-    model = wrap_cuda_model(args, model)
-
-    # Get optimizer & scheduler
-    model, optimizer, scheduler, optimizer_d, scheduler_d = init_optimizer_and_scheduler_gan(args, configs, model)
-
-    # Save init checkpoints
-    info_dict = deepcopy(configs['train_conf'])
-    save_model(model, 'init', info_dict)
-
-    # Get executor
-    executor = Executor()
-
-    # Start training loop
-    for epoch in range(info_dict['max_epoch']):
-        executor.epoch = epoch
-        train_dataset.set_epoch(epoch)
-        dist.barrier()
-        group_join = dist.new_group(backend="gloo", timeout=datetime.timedelta(seconds=args.timeout))
-        executor.train_one_epoc(model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, writer, info_dict, group_join)
-        dist.destroy_process_group(group_join)
-
-
-if __name__ == '__main__':
-    main()

cosyvoice/cli/cosyvoice.py

@@ -23,7 +23,7 @@ from cosyvoice.utils.file_utils import logging
 
 class CosyVoice:
 
-    def __init__(self, model_dir, load_jit=True, load_onnx=False):
+    def __init__(self, model_dir, load_jit=True, load_onnx=False, fp16=True):
         instruct = True if '-Instruct' in model_dir else False
         self.model_dir = model_dir
         if not os.path.exists(model_dir):
@@ -37,7 +37,7 @@ class CosyVoice:
                                           '{}/spk2info.pt'.format(model_dir),
                                           instruct,
                                           configs['allowed_special'])
-        self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'])
+        self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16)
         self.model.load('{}/llm.pt'.format(model_dir),
                         '{}/flow.pt'.format(model_dir),
                         '{}/hift.pt'.format(model_dir))

cosyvoice/cli/model.py

@@ -26,11 +26,13 @@ class CosyVoiceModel:
     def __init__(self,
                  llm: torch.nn.Module,
                  flow: torch.nn.Module,
-                 hift: torch.nn.Module):
+                 hift: torch.nn.Module,
+                 fp16: bool):
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         self.llm = llm
         self.flow = flow
         self.hift = hift
+        self.fp16 = fp16
         self.token_min_hop_len = 2 * self.flow.input_frame_rate
         self.token_max_hop_len = 4 * self.flow.input_frame_rate
         self.token_overlap_len = 20
@@ -56,13 +58,17 @@ class CosyVoiceModel:
     def load(self, llm_model, flow_model, hift_model):
         self.llm.load_state_dict(torch.load(llm_model, map_location=self.device))
         self.llm.to(self.device).eval()
-        self.llm.half()
+        if self.fp16 is True:
+            self.llm.half()
         self.flow.load_state_dict(torch.load(flow_model, map_location=self.device))
         self.flow.to(self.device).eval()
-        self.hift.load_state_dict(torch.load(hift_model, map_location=self.device))
+        # in case hift_model is a hifigan model
+        hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device)}
+        self.hift.load_state_dict(hift_state_dict, strict=False)
         self.hift.to(self.device).eval()
 
     def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model):
+        assert self.fp16 is True, "we only provide fp16 jit model, set fp16=True if you want to use jit model"
         llm_text_encoder = torch.jit.load(llm_text_encoder_model, map_location=self.device)
         self.llm.text_encoder = llm_text_encoder
         llm_llm = torch.jit.load(llm_llm_model, map_location=self.device)
@@ -80,6 +86,8 @@ class CosyVoiceModel:
         self.flow.decoder.estimator = onnxruntime.InferenceSession(flow_decoder_estimator_model, sess_options=option, providers=providers)
 
     def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
+        if self.fp16 is True:
+            llm_embedding = llm_embedding.half()
         with self.llm_context:
             for i in self.llm.inference(text=text.to(self.device),
                                         text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
@@ -87,7 +95,7 @@ class CosyVoiceModel:
                                         prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
                                         prompt_speech_token=llm_prompt_speech_token.to(self.device),
                                         prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
-                                        embedding=llm_embedding.to(self.device).half()):
+                                        embedding=llm_embedding.to(self.device)):
                 self.tts_speech_token_dict[uuid].append(i)
         self.llm_end_dict[uuid] = True
 
@@ -123,7 +131,7 @@ class CosyVoiceModel:
             if speed != 1.0:
                 assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
                 tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
-            tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
+            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
             if self.hift_cache_dict[uuid] is not None:
                 tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
         return tts_speech

cosyvoice/dataset/dataset.py

@@ -126,6 +126,7 @@ class DataList(IterableDataset):
 def Dataset(data_list_file,
             data_pipeline,
             mode='train',
+            gan=False,
             shuffle=True,
             partition=True,
             tts_file='',
@@ -153,8 +154,11 @@ def Dataset(data_list_file,
                        shuffle=shuffle,
                        partition=partition)
     if mode == 'inference':
-        # map partial arg tts_data in inference mode
+        # map partial arg to parquet_opener func in inference mode
         data_pipeline[0] = partial(data_pipeline[0], tts_data=tts_data)
+    if gan is True:
+        # map partial arg to padding func in gan mode
+        data_pipeline[-1] = partial(data_pipeline[-1], gan=gan)
     for func in data_pipeline:
         dataset = Processor(dataset, func, mode=mode)
     return dataset

cosyvoice/dataset/processor.py

@@ -350,7 +350,7 @@ def batch(data, batch_type='static', batch_size=16, max_frames_in_batch=12000, m
             logging.fatal('Unsupported batch type {}'.format(batch_type))
 
 
-def padding(data, use_spk_embedding, mode='train'):
+def padding(data, use_spk_embedding, mode='train', gan=False):
     """ Padding the data into training data
 
         Args:
@@ -379,11 +379,6 @@ def padding(data, use_spk_embedding, mode='train'):
         speech_feat = pad_sequence(speech_feat,
                                    batch_first=True,
                                    padding_value=0)
-        pitch_feat = [sample[i]['pitch_feat'] for i in order]
-        pitch_feat_len = torch.tensor([i.size(0) for i in pitch_feat], dtype=torch.int32)
-        pitch_feat = pad_sequence(pitch_feat,
-                                   batch_first=True,
-                                   padding_value=0)
         text = [sample[i]['text'] for i in order]
         text_token = [torch.tensor(sample[i]['text_token']) for i in order]
         text_token_len = torch.tensor([i.size(0) for i in text_token], dtype=torch.int32)
@@ -406,6 +401,19 @@ def padding(data, use_spk_embedding, mode='train'):
             "utt_embedding": utt_embedding,
             "spk_embedding": spk_embedding,
         }
+        if gan is True:
+            # in gan train, we need pitch_feat
+            pitch_feat = [sample[i]['pitch_feat'] for i in order]
+            pitch_feat_len = torch.tensor([i.size(0) for i in pitch_feat], dtype=torch.int32)
+            pitch_feat = pad_sequence(pitch_feat,
+                                    batch_first=True,
+                                    padding_value=0)
+            batch["pitch_feat"] = pitch_feat
+            batch["pitch_feat_len"] = pitch_feat_len
+        else:
+            # only gan train needs speech, delete it to save memory
+            del batch["speech"]
+            del batch["speech_len"]
         if mode == 'inference':
             tts_text = [sample[i]['tts_text'] for i in order]
             tts_index = [sample[i]['tts_index'] for i in order]

cosyvoice/utils/executor.py

@@ -25,7 +25,8 @@ from cosyvoice.utils.train_utils import update_parameter_and_lr, log_per_step, l
 
 class Executor:
 
-    def __init__(self):
+    def __init__(self, gan: bool=False):
+        self.gan = gan
         self.step = 0
         self.epoch = 0
         self.rank = int(os.environ.get('RANK', 0))
@@ -80,6 +81,63 @@ class Executor:
         dist.barrier()
         self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
 
+    def train_one_epoc_gan(self, model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, writer, info_dict, group_join):
+        ''' Train one epoch
+        '''
+
+        lr = optimizer.param_groups[0]['lr']
+        logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank))
+        logging.info('using accumulate grad, new batch size is {} times'
+                     ' larger than before'.format(info_dict['accum_grad']))
+        # A context manager to be used in conjunction with an instance of
+        # torch.nn.parallel.DistributedDataParallel to be able to train
+        # with uneven inputs across participating processes.
+        model.train()
+        model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext
+        with model_context():
+            for batch_idx, batch_dict in enumerate(train_data_loader):
+                info_dict["tag"] = "TRAIN"
+                info_dict["step"] = self.step
+                info_dict["epoch"] = self.epoch
+                info_dict["batch_idx"] = batch_idx
+                if cosyvoice_join(group_join, info_dict):
+                    break
+
+                # Disable gradient synchronizations across DDP processes.
+                # Within this context, gradients will be accumulated on module
+                # variables, which will later be synchronized.
+                if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0:
+                    context = model.no_sync
+                # Used for single gpu training and DDP gradient synchronization
+                # processes.
+                else:
+                    context = nullcontext
+
+                with context():
+                    batch_dict['turn'] = 'discriminator'
+                    info_dict = batch_forward(model, batch_dict, info_dict)
+                    info_dict = batch_backward(model, info_dict)
+                info_dict = update_parameter_and_lr(model, optimizer_d, scheduler_d, info_dict)
+                optimizer.zero_grad()
+                log_per_step(writer, info_dict)
+                with context():
+                    batch_dict['turn'] = 'generator'
+                    info_dict = batch_forward(model, batch_dict, info_dict)
+                    info_dict = batch_backward(model, info_dict)
+                info_dict = update_parameter_and_lr(model, optimizer, scheduler, info_dict)
+                optimizer_d.zero_grad()
+                log_per_step(writer, info_dict)
+                # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save
+                if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \
+                   (batch_idx + 1) % info_dict["accum_grad"] == 0:
+                    dist.barrier()
+                    self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False)
+                    model.train()
+                if (batch_idx + 1) % info_dict["accum_grad"] == 0:
+                    self.step += 1
+        dist.barrier()
+        self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
+
     @torch.inference_mode()
     def cv(self, model, cv_data_loader, writer, info_dict, on_batch_end=True):
         ''' Cross validation on
@@ -96,6 +154,8 @@ class Executor:
             num_utts = len(batch_dict["utts"])
             total_num_utts += num_utts
 
+            if self.gan is True:
+                batch_dict['turn'] = 'generator'
             info_dict = batch_forward(model, batch_dict, info_dict)
 
             for k, v in info_dict['loss_dict'].items():

cosyvoice/utils/executor_gan.py

@@ -1,118 +0,0 @@
-# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
-#               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.
-
-import logging
-from contextlib import nullcontext
-import os
-
-import torch
-import torch.distributed as dist
-
-from cosyvoice.utils.train_utils import update_parameter_and_lr, log_per_step, log_per_save, batch_forward, batch_backward, save_model, cosyvoice_join
-
-
-class Executor:
-
-    def __init__(self):
-        self.step = 0
-        self.epoch = 0
-        self.rank = int(os.environ.get('RANK', 0))
-        self.device = torch.device('cuda:{}'.format(self.rank))
-
-    def train_one_epoc(self, model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, writer, info_dict, group_join):
-        ''' Train one epoch
-        '''
-
-        lr = optimizer.param_groups[0]['lr']
-        logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank))
-        logging.info('using accumulate grad, new batch size is {} times'
-                     ' larger than before'.format(info_dict['accum_grad']))
-        # A context manager to be used in conjunction with an instance of
-        # torch.nn.parallel.DistributedDataParallel to be able to train
-        # with uneven inputs across participating processes.
-        model.train()
-        model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext
-        with model_context():
-            for batch_idx, batch_dict in enumerate(train_data_loader):
-                info_dict["tag"] = "TRAIN"
-                info_dict["step"] = self.step
-                info_dict["epoch"] = self.epoch
-                info_dict["batch_idx"] = batch_idx
-                if cosyvoice_join(group_join, info_dict):
-                    break
-
-                # Disable gradient synchronizations across DDP processes.
-                # Within this context, gradients will be accumulated on module
-                # variables, which will later be synchronized.
-                if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0:
-                    context = model.no_sync
-                # Used for single gpu training and DDP gradient synchronization
-                # processes.
-                else:
-                    context = nullcontext
-
-                with context():
-                    batch_dict['turn'] = 'discriminator'
-                    info_dict = batch_forward(model, batch_dict, info_dict)
-                    info_dict = batch_backward(model, info_dict)
-                info_dict = update_parameter_and_lr(model, optimizer_d, scheduler_d, info_dict)
-                log_per_step(writer, info_dict)
-                with context():
-                    batch_dict['turn'] = 'generator'
-                    info_dict = batch_forward(model, batch_dict, info_dict)
-                    info_dict = batch_backward(model, info_dict)
-                info_dict = update_parameter_and_lr(model, optimizer, scheduler, info_dict)
-                log_per_step(writer, info_dict)
-                # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save
-                if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and \
-                   (batch_idx + 1) % info_dict["accum_grad"] == 0:
-                    dist.barrier()
-                    self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False)
-                    model.train()
-                if (batch_idx + 1) % info_dict["accum_grad"] == 0:
-                    self.step += 1
-        dist.barrier()
-        self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
-
-    @torch.inference_mode()
-    def cv(self, model, cv_data_loader, writer, info_dict, on_batch_end=True):
-        ''' Cross validation on
-        '''
-        logging.info('Epoch {} Step {} on_batch_end {} CV rank {}'.format(self.epoch, self.step + 1, on_batch_end, self.rank))
-        model.eval()
-        total_num_utts, total_loss_dict = 0, {}  # avoid division by 0
-        for batch_idx, batch_dict in enumerate(cv_data_loader):
-            info_dict["tag"] = "CV"
-            info_dict["step"] = self.step
-            info_dict["epoch"] = self.epoch
-            info_dict["batch_idx"] = batch_idx
-
-            num_utts = len(batch_dict["utts"])
-            total_num_utts += num_utts
-
-            batch_dict['turn'] = 'generator'
-            info_dict = batch_forward(model, batch_dict, info_dict)
-
-            for k, v in info_dict['loss_dict'].items():
-                if k not in total_loss_dict:
-                    total_loss_dict[k] = []
-                total_loss_dict[k].append(v.item() * num_utts)
-            log_per_step(None, info_dict)
-        for k, v in total_loss_dict.items():
-            total_loss_dict[k] = sum(v) / total_num_utts
-        info_dict['loss_dict'] = total_loss_dict
-        log_per_save(writer, info_dict)
-        model_name = 'epoch_{}_whole'.format(self.epoch) if on_batch_end else 'epoch_{}_step_{}'.format(self.epoch, self.step + 1)
-        save_model(model, model_name, info_dict)

cosyvoice/utils/train_utils.py

@@ -51,9 +51,10 @@ def init_distributed(args):
     return world_size, local_rank, rank
 
 
-def init_dataset_and_dataloader(args, configs):
-    train_dataset = Dataset(args.train_data, data_pipeline=configs['data_pipeline'], mode='train', shuffle=True, partition=True)
-    cv_dataset = Dataset(args.cv_data, data_pipeline=configs['data_pipeline'], mode='train', shuffle=False, partition=False)
+def init_dataset_and_dataloader(args, configs, gan):
+    data_pipeline = configs['data_pipeline_gan'] if gan is True else configs['data_pipeline']
+    train_dataset = Dataset(args.train_data, data_pipeline=data_pipeline, mode='train', gan=gan, shuffle=True, partition=True)
+    cv_dataset = Dataset(args.cv_data, data_pipeline=data_pipeline, mode='train', gan=gan, shuffle=False, partition=False)
 
     # do not use persistent_workers=True, as whisper tokenizer opens tiktoken file each time when the for loop starts
     train_data_loader = DataLoader(train_dataset,
@@ -108,30 +109,31 @@ def wrap_cuda_model(args, model):
     return model
 
 
-def init_optimizer_and_scheduler(args, configs, model):
-    if configs['train_conf']['optim'] == 'adam':
-        optimizer = optim.Adam(model.parameters(), **configs['train_conf']['optim_conf'])
-    elif configs['train_conf']['optim'] == 'adamw':
-        optimizer = optim.AdamW(model.parameters(), **configs['train_conf']['optim_conf'])
+def init_optimizer_and_scheduler(args, configs, model, gan):
+    key = 'train_conf_gan' if gan is True else 'train_conf'
+    if configs[key]['optim'] == 'adam':
+        optimizer = optim.Adam(model.parameters(), **configs[key]['optim_conf'])
+    elif configs[key]['optim'] == 'adamw':
+        optimizer = optim.AdamW(model.parameters(), **configs[key]['optim_conf'])
     else:
-        raise ValueError("unknown optimizer: " + configs['train_conf'])
+        raise ValueError("unknown optimizer: " + configs[key])
 
-    if configs['train_conf']['scheduler'] == 'warmuplr':
+    if configs[key]['scheduler'] == 'warmuplr':
         scheduler_type = WarmupLR
-        scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf'])
-    elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing':
+        scheduler = WarmupLR(optimizer, **configs[key]['scheduler_conf'])
+    elif configs[key]['scheduler'] == 'NoamHoldAnnealing':
         scheduler_type = NoamHoldAnnealing
-        scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf'])
-    elif configs['train_conf']['scheduler'] == 'constantlr':
+        scheduler = NoamHoldAnnealing(optimizer, **configs[key]['scheduler_conf'])
+    elif configs[key]['scheduler'] == 'constantlr':
         scheduler_type = ConstantLR
         scheduler = ConstantLR(optimizer)
     else:
-        raise ValueError("unknown scheduler: " + configs['train_conf'])
+        raise ValueError("unknown scheduler: " + configs[key])
 
     # use deepspeed optimizer for speedup
     if args.train_engine == "deepspeed":
         def scheduler(opt):
-            return scheduler_type(opt, **configs['train_conf']['scheduler_conf'])
+            return scheduler_type(opt, **configs[key]['scheduler_conf'])
         model, optimizer, _, scheduler = deepspeed.initialize(
             args=args,
             model=model,
@@ -139,49 +141,28 @@ def init_optimizer_and_scheduler(args, configs, model):
             lr_scheduler=scheduler,
             model_parameters=model.parameters())
 
-    return model, optimizer, scheduler
-
-
-def init_optimizer_and_scheduler_gan(args, configs, model):
-    if configs['train_conf']['optim'] == 'adam':
-        optimizer = optim.Adam(model.module.generator.parameters(), **configs['train_conf']['optim_conf'])
-    elif configs['train_conf']['optim'] == 'adamw':
-        optimizer = optim.AdamW(model.module.generator.parameters(), **configs['train_conf']['optim_conf'])
-    else:
-        raise ValueError("unknown optimizer: " + configs['train_conf'])
-
-    if configs['train_conf']['scheduler'] == 'warmuplr':
-        scheduler_type = WarmupLR
-        scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf'])
-    elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing':
-        scheduler_type = NoamHoldAnnealing
-        scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf'])
-    elif configs['train_conf']['scheduler'] == 'constantlr':
-        scheduler_type = ConstantLR
-        scheduler = ConstantLR(optimizer)
-    else:
-        raise ValueError("unknown scheduler: " + configs['train_conf'])
-
-    if configs['train_conf']['optim_d'] == 'adam':
-        optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf'])
-    elif configs['train_conf']['optim_d'] == 'adamw':
-        optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf'])
-    else:
-        raise ValueError("unknown optimizer: " + configs['train_conf'])
-
-    if configs['train_conf']['scheduler_d'] == 'warmuplr':
-        scheduler_type = WarmupLR
-        scheduler_d = WarmupLR(optimizer_d, **configs['train_conf']['scheduler_conf'])
-    elif configs['train_conf']['scheduler_d'] == 'NoamHoldAnnealing':
-        scheduler_type = NoamHoldAnnealing
-        scheduler_d = NoamHoldAnnealing(optimizer_d, **configs['train_conf']['scheduler_conf'])
-    elif configs['train_conf']['scheduler'] == 'constantlr':
-        scheduler_type = ConstantLR
-        scheduler_d = ConstantLR(optimizer_d)
-    else:
-        raise ValueError("unknown scheduler: " + configs['train_conf'])
-
     # currently we wrap generator and discriminator in one model, so we cannot use deepspeed
+    if gan is True:
+        if configs[key]['optim_d'] == 'adam':
+            optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs[key]['optim_conf'])
+        elif configs[key]['optim_d'] == 'adamw':
+            optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs[key]['optim_conf'])
+        else:
+            raise ValueError("unknown optimizer: " + configs[key])
+
+        if configs[key]['scheduler_d'] == 'warmuplr':
+            scheduler_type = WarmupLR
+            scheduler_d = WarmupLR(optimizer_d, **configs[key]['scheduler_conf'])
+        elif configs[key]['scheduler_d'] == 'NoamHoldAnnealing':
+            scheduler_type = NoamHoldAnnealing
+            scheduler_d = NoamHoldAnnealing(optimizer_d, **configs[key]['scheduler_conf'])
+        elif configs[key]['scheduler'] == 'constantlr':
+            scheduler_type = ConstantLR
+            scheduler_d = ConstantLR(optimizer_d)
+        else:
+            raise ValueError("unknown scheduler: " + configs[key])
+    else:
+        optimizer_d, scheduler_d = None, None
     return model, optimizer, scheduler, optimizer_d, scheduler_d
 
 

examples/libritts/cosyvoice/conf/cosyvoice.hifigan.yaml

@@ -1,141 +0,0 @@
-# set random seed, so that you may reproduce your result.
-__set_seed1: !apply:random.seed [1986]
-__set_seed2: !apply:numpy.random.seed [1986]
-__set_seed3: !apply:torch.manual_seed [1986]
-__set_seed4: !apply:torch.cuda.manual_seed_all [1986]
-
-# fixed params
-sample_rate: 22050
-text_encoder_input_size: 512
-llm_input_size: 1024
-llm_output_size: 1024
-spk_embed_dim: 192
-
-# model params
-# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
-# for system/third_party class/function, we do not require this.
-hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
-    in_channels: 80
-    base_channels: 512
-    nb_harmonics: 8
-    sampling_rate: !ref <sample_rate>
-    nsf_alpha: 0.1
-    nsf_sigma: 0.003
-    nsf_voiced_threshold: 10
-    upsample_rates: [8, 8]
-    upsample_kernel_sizes: [16, 16]
-    istft_params:
-        n_fft: 16
-        hop_len: 4
-    resblock_kernel_sizes: [3, 7, 11]
-    resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
-    source_resblock_kernel_sizes: [7, 11]
-    source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
-    lrelu_slope: 0.1
-    audio_limit: 0.99
-    f0_predictor: !new:cosyvoice.hifigan.f0_predictor.ConvRNNF0Predictor
-        num_class: 1
-        in_channels: 80
-        cond_channels: 512
-
-mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram
-    n_fft: 1024
-    num_mels: 80
-    sampling_rate: !ref <sample_rate>
-    hop_size: 256
-    win_size: 1024
-    fmin: 0
-    fmax: 8000
-    center: False
-hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan
-    generator: !ref <hift>
-    discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator
-        mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator
-        mrd: !new:cosyvoice.hifigan.discriminator.MultiResolutionDiscriminator
-    mel_spec_transform: [
-        !ref <mel_spec_transform1>
-    ]
-
-# processor functions
-parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
-get_tokenizer: !name:whisper.tokenizer.get_tokenizer # change to !name:cosyvoice.tokenizer.tokenizer.get_tokenizer if you want to train with CosyVoice-300M-25Hz recipe
-    multilingual: True
-    num_languages: 100
-    language: 'en'
-    task: 'transcribe'
-tokenize: !name:cosyvoice.dataset.processor.tokenize
-    get_tokenizer: !ref <get_tokenizer>
-    allowed_special: 'all'
-filter: !name:cosyvoice.dataset.processor.filter
-    max_length: 40960
-    min_length: 0
-    token_max_length: 200
-    token_min_length: 1
-resample: !name:cosyvoice.dataset.processor.resample
-    resample_rate: !ref <sample_rate>
-truncate: !name:cosyvoice.dataset.processor.truncate
-    truncate_length: 24576 # must be a multiplier of hop_size
-feat_extractor: !name:matcha.utils.audio.mel_spectrogram
-    n_fft: 1024
-    num_mels: 80
-    sampling_rate: !ref <sample_rate>
-    hop_size: 256
-    win_size: 1024
-    fmin: 0
-    fmax: 8000
-    center: False
-compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank
-    feat_extractor: !ref <feat_extractor>
-pitch_extractor: !name:torchaudio.functional.compute_kaldi_pitch
-    sample_rate: !ref <sample_rate>
-    frame_length: 46.4 # match feat_extractor win_size/sampling_rate
-    frame_shift: 11.6 # match feat_extractor hop_size/sampling_rate
-compute_f0: !name:cosyvoice.dataset.processor.compute_f0
-    pitch_extractor: !ref <pitch_extractor>
-parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding
-    normalize: True
-shuffle: !name:cosyvoice.dataset.processor.shuffle
-    shuffle_size: 1000
-sort: !name:cosyvoice.dataset.processor.sort
-    sort_size: 500  # sort_size should be less than shuffle_size
-batch: !name:cosyvoice.dataset.processor.batch
-    batch_type: 'dynamic'
-    max_frames_in_batch: 1200
-padding: !name:cosyvoice.dataset.processor.padding
-    use_spk_embedding: False # change to True during sft
-
-# dataset processor pipeline
-data_pipeline: [
-    !ref <parquet_opener>,
-    !ref <tokenize>,
-    !ref <filter>,
-    !ref <resample>,
-    !ref <truncate>,
-    !ref <compute_fbank>,
-    !ref <compute_f0>,
-    !ref <parse_embedding>,
-    !ref <shuffle>,
-    !ref <sort>,
-    !ref <batch>,
-    !ref <padding>,
-]
-
-# train conf
-train_conf:
-    optim: adam
-    optim_conf:
-        lr: 0.002 # change to 0.001 if you want to train flow from scratch
-    scheduler: warmuplr
-    scheduler_conf:
-        warmup_steps: 25000
-    optim_d: adam
-    optim_conf_d:
-        lr: 0.002 # change to 0.001 if you want to train flow from scratch
-    scheduler_d: warmuplr
-    scheduler_conf_d:
-        warmup_steps: 25000
-    max_epoch: 200
-    grad_clip: 5
-    accum_grad: 2
-    log_interval: 100
-    save_per_step: -1

examples/libritts/cosyvoice/conf/cosyvoice.yaml

@@ -133,6 +133,25 @@ hift: !new:cosyvoice.hifigan.generator.HiFTGenerator
         in_channels: 80
         cond_channels: 512
 
+# gan related module
+mel_spec_transform1: !name:matcha.utils.audio.mel_spectrogram
+    n_fft: 1024
+    num_mels: 80
+    sampling_rate: !ref <sample_rate>
+    hop_size: 256
+    win_size: 1024
+    fmin: 0
+    fmax: 8000
+    center: False
+hifigan: !new:cosyvoice.hifigan.hifigan.HiFiGan
+    generator: !ref <hift>
+    discriminator: !new:cosyvoice.hifigan.discriminator.MultipleDiscriminator
+        mpd: !new:matcha.hifigan.models.MultiPeriodDiscriminator
+        mrd: !new:cosyvoice.hifigan.discriminator.MultiResolutionDiscriminator
+    mel_spec_transform: [
+        !ref <mel_spec_transform1>
+    ]
+
 # processor functions
 parquet_opener: !name:cosyvoice.dataset.processor.parquet_opener
 get_tokenizer: !name:whisper.tokenizer.get_tokenizer # change to !name:cosyvoice.tokenizer.tokenizer.get_tokenizer if you want to train with CosyVoice-300M-25Hz recipe
@@ -151,6 +170,8 @@ filter: !name:cosyvoice.dataset.processor.filter
     token_min_length: 1
 resample: !name:cosyvoice.dataset.processor.resample
     resample_rate: !ref <sample_rate>
+truncate: !name:cosyvoice.dataset.processor.truncate
+    truncate_length: 24576 # must be a multiplier of hop_size
 feat_extractor: !name:matcha.utils.audio.mel_spectrogram
     n_fft: 1024
     num_mels: 80
@@ -162,6 +183,12 @@ feat_extractor: !name:matcha.utils.audio.mel_spectrogram
     center: False
 compute_fbank: !name:cosyvoice.dataset.processor.compute_fbank
     feat_extractor: !ref <feat_extractor>
+pitch_extractor: !name:torchaudio.functional.compute_kaldi_pitch
+    sample_rate: !ref <sample_rate>
+    frame_length: 46.4 # match feat_extractor win_size/sampling_rate
+    frame_shift: 11.6 # match feat_extractor hop_size/sampling_rate
+compute_f0: !name:cosyvoice.dataset.processor.compute_f0
+    pitch_extractor: !ref <pitch_extractor>
 parse_embedding: !name:cosyvoice.dataset.processor.parse_embedding
     normalize: True
 shuffle: !name:cosyvoice.dataset.processor.shuffle
@@ -187,8 +214,22 @@ data_pipeline: [
     !ref <batch>,
     !ref <padding>,
 ]
+data_pipeline_gan: [
+    !ref <parquet_opener>,
+    !ref <tokenize>,
+    !ref <filter>,
+    !ref <resample>,
+    !ref <truncate>,
+    !ref <compute_fbank>,
+    !ref <compute_f0>,
+    !ref <parse_embedding>,
+    !ref <shuffle>,
+    !ref <sort>,
+    !ref <batch>,
+    !ref <padding>,
+]
 
-# train conf
+# llm flow train conf
 train_conf:
     optim: adam
     optim_conf:
@@ -200,4 +241,20 @@ train_conf:
     grad_clip: 5
     accum_grad: 2
     log_interval: 100
+    save_per_step: -1
+
+# gan train conf
+train_conf_gan:
+    optim: adam
+    optim_conf:
+        lr: 0.0002 # use small lr for gan training
+    scheduler: constantlr
+    optim_d: adam
+    optim_conf_d:
+        lr: 0.0002 # use small lr for gan training
+    scheduler_d: constantlr
+    max_epoch: 200
+    grad_clip: 5
+    accum_grad: 1 # in gan training, accum_grad must be 1
+    log_interval: 100
     save_per_step: -1

examples/libritts/cosyvoice/run.sh

@@ -83,9 +83,9 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
   fi
   cat data/{train-clean-100,train-clean-360,train-other-500}/parquet/data.list > data/train.data.list
   cat data/{dev-clean,dev-other}/parquet/data.list > data/dev.data.list
-  for model in llm flow; do
+  for model in llm flow hifigan; do
     torchrun --nnodes=1 --nproc_per_node=$num_gpus \
-        --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:0" \
+        --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:1234" \
       cosyvoice/bin/train.py \
       --train_engine $train_engine \
       --config conf/cosyvoice.yaml \
@@ -99,12 +99,27 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
       --num_workers ${num_workers} \
       --prefetch ${prefetch} \
       --pin_memory \
+      --timeout 300 \
       --deepspeed_config ./conf/ds_stage2.json \
       --deepspeed.save_states model+optimizer
   done
 fi
 
+# average model
+average_num=5
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
+  for model in llm flow hifigan; do
+    decode_checkpoint=`pwd`/exp/cosyvoice/$model/$train_engine/${model}.pt
+    echo "do model average and final checkpoint is $decode_checkpoint"
+    python cosyvoice/bin/average_model.py \
+      --dst_model $decode_checkpoint \
+      --src_path `pwd`/exp/cosyvoice/$model/$train_engine  \
+      --num ${average_num} \
+      --val_best
+  done
+fi
+
+if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
   echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir"
   python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir
   python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir