Upload folder using huggingface_hub (part 10)

.gitattributes

@@ -544,3 +544,9 @@ third_party/BindWeave/s2v/OpenS2V-Eval/Results/singleface.mp4 filter=lfs diff=lf
 third_party/CosyVoice/asset/zero_shot_prompt.wav filter=lfs diff=lfs merge=lfs -text
 third_party/CosyVoice/asset/龙小夏.mp3 filter=lfs diff=lfs merge=lfs -text
 third_party/CosyVoice/asset/龙小夏_24k.wav filter=lfs diff=lfs merge=lfs -text
+third_party/DiffSynth-Studio/diffsynth/pipelines/__pycache__/wan_video.cpython-313.pyc filter=lfs diff=lfs merge=lfs -text
+vgen/__pycache__/poetry_teaching-baseline.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
+vgen/__pycache__/poetry_teaching-baseline.cpython-313.pyc filter=lfs diff=lfs merge=lfs -text
+vgen/__pycache__/poetry_teaching-planloop.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
+vgen/__pycache__/poetry_teaching-planloop.cpython-313.pyc filter=lfs diff=lfs merge=lfs -text
+vgen/__pycache__/poetry_teaching.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text

third_party/CosyVoice/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 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 ['step', 'epoch']:
+                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()

third_party/CosyVoice/cosyvoice/bin/export_jit.py

@@ -0,0 +1,99 @@
+# 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 sys
+import torch
+ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append('{}/../..'.format(ROOT_DIR))
+sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
+from cosyvoice.cli.cosyvoice import AutoModel
+from cosyvoice.utils.file_utils import logging
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='export your model for deployment')
+    parser.add_argument('--model_dir',
+                        type=str,
+                        default='pretrained_models/CosyVoice-300M',
+                        help='local path')
+    args = parser.parse_args()
+    print(args)
+    return args
+
+
+def get_optimized_script(model, preserved_attrs=[]):
+    script = torch.jit.script(model)
+    if preserved_attrs != []:
+        script = torch.jit.freeze(script, preserved_attrs=preserved_attrs)
+    else:
+        script = torch.jit.freeze(script)
+    script = torch.jit.optimize_for_inference(script)
+    return script
+
+
+def main():
+    args = get_args()
+    logging.basicConfig(level=logging.DEBUG,
+                        format='%(asctime)s %(levelname)s %(message)s')
+
+    torch._C._jit_set_fusion_strategy([('STATIC', 1)])
+    torch._C._jit_set_profiling_mode(False)
+    torch._C._jit_set_profiling_executor(False)
+
+    model = AutoModel(model_dir=args.model_dir)
+
+    if model.__class__.__name__ == 'CosyVoice':
+        # 1. export llm text_encoder
+        llm_text_encoder = model.model.llm.text_encoder
+        script = get_optimized_script(llm_text_encoder)
+        script.save('{}/llm.text_encoder.fp32.zip'.format(args.model_dir))
+        script = get_optimized_script(llm_text_encoder.half())
+        script.save('{}/llm.text_encoder.fp16.zip'.format(args.model_dir))
+        logging.info('successfully export llm_text_encoder')
+
+        # 2. export llm llm
+        llm_llm = model.model.llm.llm
+        script = get_optimized_script(llm_llm, ['forward_chunk'])
+        script.save('{}/llm.llm.fp32.zip'.format(args.model_dir))
+        script = get_optimized_script(llm_llm.half(), ['forward_chunk'])
+        script.save('{}/llm.llm.fp16.zip'.format(args.model_dir))
+        logging.info('successfully export llm_llm')
+
+        # 3. export flow encoder
+        flow_encoder = model.model.flow.encoder
+        script = get_optimized_script(flow_encoder)
+        script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
+        script = get_optimized_script(flow_encoder.half())
+        script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
+        logging.info('successfully export flow_encoder')
+    elif model.__class__.__name__ == 'CosyVoice2':
+        # 1. export flow encoder
+        flow_encoder = model.model.flow.encoder
+        script = get_optimized_script(flow_encoder)
+        script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
+        script = get_optimized_script(flow_encoder.half())
+        script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
+        logging.info('successfully export flow_encoder')
+    else:
+        raise ValueError('unsupported model type')
+
+
+if __name__ == '__main__':
+    main()

third_party/CosyVoice/cosyvoice/bin/export_onnx.py

@@ -0,0 +1,114 @@
+# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, hexisyztem@icloud.com)
+# 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 sys
+import onnxruntime
+import random
+import torch
+from tqdm import tqdm
+ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append('{}/../..'.format(ROOT_DIR))
+sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
+from cosyvoice.cli.cosyvoice import AutoModel
+from cosyvoice.utils.file_utils import logging
+
+
+def get_dummy_input(batch_size, seq_len, out_channels, device):
+    x = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+    mask = torch.ones((batch_size, 1, seq_len), dtype=torch.float32, device=device)
+    mu = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+    t = torch.rand((batch_size), dtype=torch.float32, device=device)
+    spks = torch.rand((batch_size, out_channels), dtype=torch.float32, device=device)
+    cond = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
+    return x, mask, mu, t, spks, cond
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='export your model for deployment')
+    parser.add_argument('--model_dir',
+                        type=str,
+                        default='pretrained_models/CosyVoice-300M',
+                        help='local path')
+    args = parser.parse_args()
+    print(args)
+    return args
+
+
+@torch.no_grad()
+def main():
+    args = get_args()
+    logging.basicConfig(level=logging.DEBUG,
+                        format='%(asctime)s %(levelname)s %(message)s')
+
+    model = AutoModel(model_dir=args.model_dir)
+
+    # 1. export flow decoder estimator
+    estimator = model.model.flow.decoder.estimator
+    estimator.eval()
+
+    device = model.model.device
+    batch_size, seq_len = 2, 256
+    out_channels = model.model.flow.decoder.estimator.out_channels
+    x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device)
+    torch.onnx.export(
+        estimator,
+        (x, mask, mu, t, spks, cond),
+        '{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+        export_params=True,
+        opset_version=18,
+        do_constant_folding=True,
+        input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'],
+        output_names=['estimator_out'],
+        dynamic_axes={
+            'x': {2: 'seq_len'},
+            'mask': {2: 'seq_len'},
+            'mu': {2: 'seq_len'},
+            'cond': {2: 'seq_len'},
+            'estimator_out': {2: 'seq_len'},
+        }
+    )
+
+    # 2. test computation consistency
+    option = onnxruntime.SessionOptions()
+    option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+    option.intra_op_num_threads = 1
+    providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
+    estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
+                                                  sess_options=option, providers=providers)
+
+    for _ in tqdm(range(10)):
+        x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device)
+        output_pytorch = estimator(x, mask, mu, t, spks, cond)
+        ort_inputs = {
+            'x': x.cpu().numpy(),
+            'mask': mask.cpu().numpy(),
+            'mu': mu.cpu().numpy(),
+            't': t.cpu().numpy(),
+            'spks': spks.cpu().numpy(),
+            'cond': cond.cpu().numpy()
+        }
+        output_onnx = estimator_onnx.run(None, ort_inputs)[0]
+        torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4)
+    logging.info('successfully export estimator')
+
+
+if __name__ == "__main__":
+    main()

third_party/CosyVoice/cosyvoice/bin/train.py

@@ -0,0 +1,195 @@
+# 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 os
+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.losses import DPOLoss
+from cosyvoice.utils.executor import Executor
+from cosyvoice.utils.train_utils import (
+    init_distributed,
+    init_dataset_and_dataloader,
+    init_optimizer_and_scheduler,
+    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('--ref_model', required=False, help='ref model used in dpo')
+    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('--qwen_pretrain_path', required=False, help='qwen pretrain path')
+    parser.add_argument('--onnx_path', required=False, help='onnx path, which is required for online feature extraction')
+    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('--use_amp',
+                        action='store_true',
+                        default=False,
+                        help='Use automatic mixed precision training')
+    parser.add_argument('--dpo',
+                        action='store_true',
+                        default=False,
+                        help='Use Direct Preference Optimization')
+    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=60,
+                        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()
+    os.environ['onnx_path'] = args.onnx_path
+    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', 'hift', 'hifigan'] if k != args.model}
+    if gan is True:
+        override_dict.pop('hift')
+    if args.qwen_pretrain_path is not None:
+        override_dict['qwen_pretrain_path'] = args.qwen_pretrain_path
+    with open(args.config, 'r') as f:
+        configs = load_hyperpyyaml(f, overrides=override_dict)
+    if gan is True:
+        configs['train_conf'] = configs['train_conf_gan']
+    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, gan, args.dpo)
+
+    # 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
+    if args.dpo is True:
+        configs[args.model].forward = configs[args.model].forward_dpo
+    model = configs[args.model]
+    start_step, start_epoch = 0, -1
+    if args.checkpoint is not None:
+        if os.path.exists(args.checkpoint):
+            state_dict = torch.load(args.checkpoint, map_location='cpu')
+            model.load_state_dict(state_dict, strict=False)
+            if 'step' in state_dict:
+                start_step = state_dict['step']
+            if 'epoch' in state_dict:
+                start_epoch = state_dict['epoch']
+        else:
+            logging.warning('checkpoint {} do not exsist!'.format(args.checkpoint))
+
+    # 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(args, configs, model, gan)
+    scheduler.set_step(start_step)
+    if scheduler_d is not None:
+        scheduler_d.set_step(start_step)
+
+    # Save init checkpoints
+    info_dict = deepcopy(configs['train_conf'])
+    info_dict['step'] = start_step
+    info_dict['epoch'] = start_epoch
+    save_model(model, 'init', info_dict)
+
+    # DPO related
+    if args.dpo is True:
+        ref_model = deepcopy(configs[args.model])
+        state_dict = torch.load(args.ref_model, map_location='cpu')
+        ref_model.load_state_dict(state_dict, strict=False)
+        dpo_loss = DPOLoss(beta=0.01, label_smoothing=0.0, ipo=False)
+        # NOTE maybe it is not needed to wrap ref_model as ddp because its parameter is not updated
+        ref_model = wrap_cuda_model(args, ref_model)
+    else:
+        ref_model, dpo_loss = None, None
+
+    # Get executor
+    executor = Executor(gan=gan, ref_model=ref_model, dpo_loss=dpo_loss)
+    executor.step = start_step
+
+    # Init scaler, used for pytorch amp mixed precision training
+    scaler = torch.cuda.amp.GradScaler() if args.use_amp else None
+    print('start step {} start epoch {}'.format(start_step, start_epoch))
+
+    # Start training loop
+    for epoch in range(start_epoch + 1, 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))
+        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, scaler, group_join)
+        else:
+            executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, scaler, group_join, ref_model=ref_model)
+        dist.destroy_process_group(group_join)
+
+
+if __name__ == '__main__':
+    main()

third_party/CosyVoice/cosyvoice/cli/cosyvoice.py

@@ -0,0 +1,238 @@
+# 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 time
+from typing import Generator
+from tqdm import tqdm
+from hyperpyyaml import load_hyperpyyaml
+from modelscope import snapshot_download
+import torch
+from cosyvoice.cli.frontend import CosyVoiceFrontEnd
+from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, CosyVoice3Model
+from cosyvoice.utils.file_utils import logging
+from cosyvoice.utils.class_utils import get_model_type
+
+
+class CosyVoice:
+
+    def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1):
+        self.model_dir = model_dir
+        self.fp16 = fp16
+        if not os.path.exists(model_dir):
+            model_dir = snapshot_download(model_dir)
+        hyper_yaml_path = '{}/cosyvoice.yaml'.format(model_dir)
+        if not os.path.exists(hyper_yaml_path):
+            raise ValueError('{} not found!'.format(hyper_yaml_path))
+        with open(hyper_yaml_path, 'r') as f:
+            configs = load_hyperpyyaml(f)
+        assert get_model_type(configs) == CosyVoiceModel, 'do not use {} for CosyVoice initialization!'.format(model_dir)
+        self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
+                                          configs['feat_extractor'],
+                                          '{}/campplus.onnx'.format(model_dir),
+                                          '{}/speech_tokenizer_v1.onnx'.format(model_dir),
+                                          '{}/spk2info.pt'.format(model_dir),
+                                          configs['allowed_special'])
+        self.sample_rate = configs['sample_rate']
+        if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
+            load_jit, load_trt, fp16 = False, False, False
+            logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
+        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))
+        if load_jit:
+            self.model.load_jit('{}/llm.text_encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+                                '{}/llm.llm.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+                                '{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
+        if load_trt:
+            self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+                                '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
+                                trt_concurrent,
+                                self.fp16)
+        del configs
+
+    def list_available_spks(self):
+        spks = list(self.frontend.spk2info.keys())
+        return spks
+
+    def add_zero_shot_spk(self, prompt_text, prompt_wav, zero_shot_spk_id):
+        assert zero_shot_spk_id != '', 'do not use empty zero_shot_spk_id'
+        model_input = self.frontend.frontend_zero_shot('', prompt_text, prompt_wav, self.sample_rate, '')
+        del model_input['text']
+        del model_input['text_len']
+        self.frontend.spk2info[zero_shot_spk_id] = model_input
+        return True
+
+    def save_spkinfo(self):
+        torch.save(self.frontend.spk2info, '{}/spk2info.pt'.format(self.model_dir))
+
+    def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
+        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+            model_input = self.frontend.frontend_sft(i, spk_id)
+            start_time = time.time()
+            logging.info('synthesis text {}'.format(i))
+            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                yield model_output
+                start_time = time.time()
+
+    def inference_zero_shot(self, tts_text, prompt_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
+        prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
+        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+            if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text):
+                logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
+            model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_wav, self.sample_rate, zero_shot_spk_id)
+            start_time = time.time()
+            logging.info('synthesis text {}'.format(i))
+            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                yield model_output
+                start_time = time.time()
+
+    def inference_cross_lingual(self, tts_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
+        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+            model_input = self.frontend.frontend_cross_lingual(i, prompt_wav, self.sample_rate, zero_shot_spk_id)
+            start_time = time.time()
+            logging.info('synthesis text {}'.format(i))
+            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                yield model_output
+                start_time = time.time()
+
+    def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
+        assert self.__class__.__name__ == 'CosyVoice', 'inference_instruct is only implemented for CosyVoice!'
+        instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend)
+        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+            model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
+            start_time = time.time()
+            logging.info('synthesis text {}'.format(i))
+            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                yield model_output
+                start_time = time.time()
+
+    def inference_vc(self, source_wav, prompt_wav, stream=False, speed=1.0):
+        model_input = self.frontend.frontend_vc(source_wav, prompt_wav, self.sample_rate)
+        start_time = time.time()
+        for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+            speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+            logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+            yield model_output
+            start_time = time.time()
+
+
+class CosyVoice2(CosyVoice):
+
+    def __init__(self, model_dir, load_jit=False, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1):
+        self.model_dir = model_dir
+        self.fp16 = fp16
+        if not os.path.exists(model_dir):
+            model_dir = snapshot_download(model_dir)
+        hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir)
+        if not os.path.exists(hyper_yaml_path):
+            raise ValueError('{} not found!'.format(hyper_yaml_path))
+        with open(hyper_yaml_path, 'r') as f:
+            configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
+        assert get_model_type(configs) == CosyVoice2Model, 'do not use {} for CosyVoice2 initialization!'.format(model_dir)
+        self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
+                                          configs['feat_extractor'],
+                                          '{}/campplus.onnx'.format(model_dir),
+                                          '{}/speech_tokenizer_v2.onnx'.format(model_dir),
+                                          '{}/spk2info.pt'.format(model_dir),
+                                          configs['allowed_special'])
+        self.sample_rate = configs['sample_rate']
+        if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or load_vllm is True or fp16 is True):
+            load_jit, load_trt, load_vllm, fp16 = False, False, False, False
+            logging.warning('no cuda device, set load_jit/load_trt/load_vllm/fp16 to False')
+        self.model = CosyVoice2Model(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))
+        if load_vllm:
+            self.model.load_vllm('{}/vllm'.format(model_dir))
+        if load_jit:
+            self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
+        if load_trt:
+            self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+                                '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
+                                trt_concurrent,
+                                self.fp16)
+        del configs
+
+    def inference_instruct2(self, tts_text, instruct_text, prompt_wav, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
+        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+            model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_wav, self.sample_rate, zero_shot_spk_id)
+            start_time = time.time()
+            logging.info('synthesis text {}'.format(i))
+            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                yield model_output
+                start_time = time.time()
+
+
+class CosyVoice3(CosyVoice2):
+
+    def __init__(self, model_dir, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1):
+        self.model_dir = model_dir
+        self.fp16 = fp16
+        if not os.path.exists(model_dir):
+            model_dir = snapshot_download(model_dir)
+        hyper_yaml_path = '{}/cosyvoice3.yaml'.format(model_dir)
+        if not os.path.exists(hyper_yaml_path):
+            raise ValueError('{} not found!'.format(hyper_yaml_path))
+        with open(hyper_yaml_path, 'r') as f:
+            configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
+        assert get_model_type(configs) == CosyVoice3Model, 'do not use {} for CosyVoice3 initialization!'.format(model_dir)
+        self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
+                                          configs['feat_extractor'],
+                                          '{}/campplus.onnx'.format(model_dir),
+                                          '{}/speech_tokenizer_v3.onnx'.format(model_dir),
+                                          '{}/spk2info.pt'.format(model_dir),
+                                          configs['allowed_special'])
+        self.sample_rate = configs['sample_rate']
+        if torch.cuda.is_available() is False and (load_trt is True or fp16 is True):
+            load_trt, fp16 = False, False
+            logging.warning('no cuda device, set load_trt/fp16 to False')
+        self.model = CosyVoice3Model(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))
+        if load_vllm:
+            self.model.load_vllm('{}/vllm'.format(model_dir))
+        if load_trt:
+            if self.fp16 is True:
+                logging.warning('DiT tensorRT fp16 engine have some performance issue, use at caution!')
+            self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
+                                '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
+                                trt_concurrent,
+                                self.fp16)
+        del configs
+
+
+def AutoModel(**kwargs):
+    if not os.path.exists(kwargs['model_dir']):
+        kwargs['model_dir'] = snapshot_download(kwargs['model_dir'])
+    if os.path.exists('{}/cosyvoice.yaml'.format(kwargs['model_dir'])):
+        return CosyVoice(**kwargs)
+    elif os.path.exists('{}/cosyvoice2.yaml'.format(kwargs['model_dir'])):
+        return CosyVoice2(**kwargs)
+    elif os.path.exists('{}/cosyvoice3.yaml'.format(kwargs['model_dir'])):
+        return CosyVoice3(**kwargs)
+    else:
+        raise TypeError('No valid model type found!')

third_party/CosyVoice/cosyvoice/cli/frontend.py

@@ -0,0 +1,224 @@
+# 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 functools import partial
+from typing import Generator
+import json
+import onnxruntime
+import torch
+import numpy as np
+import whisper
+from typing import Callable
+import torchaudio.compliance.kaldi as kaldi
+import os
+import re
+import inflect
+from cosyvoice.utils.file_utils import logging, load_wav
+from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph, is_only_punctuation
+
+
+class CosyVoiceFrontEnd:
+
+    def __init__(self,
+                 get_tokenizer: Callable,
+                 feat_extractor: Callable,
+                 campplus_model: str,
+                 speech_tokenizer_model: str,
+                 spk2info: str = '',
+                 allowed_special: str = 'all'):
+        self.tokenizer = get_tokenizer()
+        self.feat_extractor = feat_extractor
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        option = onnxruntime.SessionOptions()
+        option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+        option.intra_op_num_threads = 1
+        self.campplus_session = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"])
+        self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option,
+                                                                     providers=["CUDAExecutionProvider" if torch.cuda.is_available() else
+                                                                                "CPUExecutionProvider"])
+        if os.path.exists(spk2info):
+            self.spk2info = torch.load(spk2info, map_location=self.device, weights_only=True)
+        else:
+            self.spk2info = {}
+        self.allowed_special = allowed_special
+        self.inflect_parser = inflect.engine()
+        # NOTE compatible when no text frontend tool is avaliable
+        try:
+            import ttsfrd
+            self.frd = ttsfrd.TtsFrontendEngine()
+            ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+            assert self.frd.initialize('{}/../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR)) is True, \
+                'failed to initialize ttsfrd resource'
+            self.frd.set_lang_type('pinyinvg')
+            self.text_frontend = 'ttsfrd'
+            logging.info('use ttsfrd frontend')
+        except:
+            try:
+                from wetext import Normalizer as ZhNormalizer
+                from wetext import Normalizer as EnNormalizer
+                self.zh_tn_model = ZhNormalizer(remove_erhua=False)
+                self.en_tn_model = EnNormalizer()
+                self.text_frontend = 'wetext'
+                logging.info('use wetext frontend')
+            except:
+                self.text_frontend = ''
+                logging.info('no frontend is avaliable')
+
+
+    def _extract_text_token(self, text):
+        if isinstance(text, Generator):
+            logging.info('get tts_text generator, will return _extract_text_token_generator!')
+            # NOTE add a dummy text_token_len for compatibility
+            return self._extract_text_token_generator(text), torch.tensor([0], dtype=torch.int32).to(self.device)
+        else:
+            text_token = self.tokenizer.encode(text, allowed_special=self.allowed_special)
+            text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device)
+            text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(self.device)
+            return text_token, text_token_len
+
+    def _extract_text_token_generator(self, text_generator):
+        for text in text_generator:
+            text_token, _ = self._extract_text_token(text)
+            for i in range(text_token.shape[1]):
+                yield text_token[:, i: i + 1]
+
+    def _extract_speech_token(self, prompt_wav):
+        speech = load_wav(prompt_wav, 16000)
+        assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s'
+        feat = whisper.log_mel_spectrogram(speech, n_mels=128)
+        speech_token = self.speech_tokenizer_session.run(None,
+                                                         {self.speech_tokenizer_session.get_inputs()[0].name:
+                                                          feat.detach().cpu().numpy(),
+                                                          self.speech_tokenizer_session.get_inputs()[1].name:
+                                                          np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist()
+        speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device)
+        speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(self.device)
+        return speech_token, speech_token_len
+
+    def _extract_spk_embedding(self, prompt_wav):
+        speech = load_wav(prompt_wav, 16000)
+        feat = kaldi.fbank(speech,
+                           num_mel_bins=80,
+                           dither=0,
+                           sample_frequency=16000)
+        feat = feat - feat.mean(dim=0, keepdim=True)
+        embedding = self.campplus_session.run(None,
+                                              {self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
+        embedding = torch.tensor([embedding]).to(self.device)
+        return embedding
+
+    def _extract_speech_feat(self, prompt_wav):
+        speech = load_wav(prompt_wav, 24000)
+        speech_feat = self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device)
+        speech_feat = speech_feat.unsqueeze(dim=0)
+        speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device)
+        return speech_feat, speech_feat_len
+
+    def text_normalize(self, text, split=True, text_frontend=True):
+        if isinstance(text, Generator):
+            logging.info('get tts_text generator, will skip text_normalize!')
+            return [text]
+        # NOTE skip text_frontend when ssml symbol in text
+        if '<|' in text and '|>' in text:
+            text_frontend = False
+        if text_frontend is False or text == '':
+            return [text] if split is True else text
+        text = text.strip()
+        if self.text_frontend == 'ttsfrd':
+            texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]]
+            text = ''.join(texts)
+        else:
+            if contains_chinese(text):
+                if self.text_frontend == 'wetext':
+                    text = self.zh_tn_model.normalize(text)
+                text = text.replace("\n", "")
+                text = replace_blank(text)
+                text = replace_corner_mark(text)
+                text = text.replace(".", "。")
+                text = text.replace(" - ", "，")
+                text = remove_bracket(text)
+                text = re.sub(r'[，,、]+$', '。', text)
+                texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80,
+                                             token_min_n=60, merge_len=20, comma_split=False))
+            else:
+                if self.text_frontend == 'wetext':
+                    text = self.en_tn_model.normalize(text)
+                text = spell_out_number(text, self.inflect_parser)
+                texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80,
+                                             token_min_n=60, merge_len=20, comma_split=False))
+        texts = [i for i in texts if not is_only_punctuation(i)]
+        return texts if split is True else text
+
+    def frontend_sft(self, tts_text, spk_id):
+        tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
+        embedding = self.spk2info[spk_id]['embedding']
+        model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding}
+        return model_input
+
+    def frontend_zero_shot(self, tts_text, prompt_text, prompt_wav, resample_rate, zero_shot_spk_id):
+        tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
+        if zero_shot_spk_id == '':
+            prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text)
+            speech_feat, speech_feat_len = self._extract_speech_feat(prompt_wav)
+            speech_token, speech_token_len = self._extract_speech_token(prompt_wav)
+            if resample_rate == 24000:
+                # cosyvoice2, force speech_feat % speech_token = 2
+                token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1])
+                speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2 * token_len
+                speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len
+            embedding = self._extract_spk_embedding(prompt_wav)
+            model_input = {'prompt_text': prompt_text_token, 'prompt_text_len': prompt_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': embedding, 'flow_embedding': embedding}
+        else:
+            model_input = {**self.spk2info[zero_shot_spk_id]}
+        model_input['text'] = tts_text_token
+        model_input['text_len'] = tts_text_token_len
+        return model_input
+
+    def frontend_cross_lingual(self, tts_text, prompt_wav, resample_rate, zero_shot_spk_id):
+        model_input = self.frontend_zero_shot(tts_text, '', prompt_wav, resample_rate, zero_shot_spk_id)
+        # in cross lingual mode, we remove prompt in llm
+        del model_input['prompt_text']
+        del model_input['prompt_text_len']
+        del model_input['llm_prompt_speech_token']
+        del model_input['llm_prompt_speech_token_len']
+        return model_input
+
+    def frontend_instruct(self, tts_text, spk_id, instruct_text):
+        model_input = self.frontend_sft(tts_text, spk_id)
+        # in instruct mode, we remove spk_embedding in llm due to information leakage
+        del model_input['llm_embedding']
+        instruct_text_token, instruct_text_token_len = self._extract_text_token(instruct_text)
+        model_input['prompt_text'] = instruct_text_token
+        model_input['prompt_text_len'] = instruct_text_token_len
+        return model_input
+
+    def frontend_instruct2(self, tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id):
+        model_input = self.frontend_zero_shot(tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id)
+        del model_input['llm_prompt_speech_token']
+        del model_input['llm_prompt_speech_token_len']
+        return model_input
+
+    def frontend_vc(self, source_speech_16k, prompt_wav, resample_rate):
+        prompt_speech_token, prompt_speech_token_len = self._extract_speech_token(prompt_wav)
+        prompt_speech_feat, prompt_speech_feat_len = self._extract_speech_feat(prompt_wav)
+        embedding = self._extract_spk_embedding(prompt_wav)
+        source_speech_token, source_speech_token_len = self._extract_speech_token(source_speech_16k)
+        model_input = {'source_speech_token': source_speech_token, 'source_speech_token_len': source_speech_token_len,
+                       'flow_prompt_speech_token': prompt_speech_token, 'flow_prompt_speech_token_len': prompt_speech_token_len,
+                       'prompt_speech_feat': prompt_speech_feat, 'prompt_speech_feat_len': prompt_speech_feat_len,
+                       'flow_embedding': embedding}
+        return model_input

third_party/CosyVoice/cosyvoice/cli/model.py

@@ -0,0 +1,467 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#               2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
+#
+# 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
+from typing import Generator
+import torch
+import numpy as np
+import threading
+import time
+from torch.nn import functional as F
+from contextlib import nullcontext
+import uuid
+from cosyvoice.utils.common import fade_in_out
+from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm
+from cosyvoice.utils.common import TrtContextWrapper
+
+
+class CosyVoiceModel:
+
+    def __init__(self,
+                 llm: torch.nn.Module,
+                 flow: torch.nn.Module,
+                 hift: torch.nn.Module,
+                 fp16: bool = False):
+        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
+        # mel fade in out
+        self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
+        self.mel_window = np.hamming(2 * self.mel_overlap_len)
+        # hift cache
+        self.mel_cache_len = 20
+        self.source_cache_len = int(self.mel_cache_len * 256)
+        # speech fade in out
+        self.speech_window = np.hamming(2 * self.source_cache_len)
+        # rtf and decoding related
+        self.stream_scale_factor = 1
+        assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
+        self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
+        self.lock = threading.Lock()
+        # dict used to store session related variable
+        self.tts_speech_token_dict = {}
+        self.llm_end_dict = {}
+        self.mel_overlap_dict = {}
+        self.flow_cache_dict = {}
+        self.hift_cache_dict = {}
+        self.llm_exception_dict = {}
+        self.silent_tokens = []
+
+    def load(self, llm_model, flow_model, hift_model):
+        self.llm.load_state_dict(torch.load(llm_model, map_location=self.device, weights_only=True), strict=True)
+        self.llm.to(self.device).eval()
+        self.flow.load_state_dict(torch.load(flow_model, map_location=self.device, weights_only=True), strict=True)
+        self.flow.to(self.device).eval()
+        # 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, weights_only=True).items()}
+        self.hift.load_state_dict(hift_state_dict, strict=True)
+        self.hift.to(self.device).eval()
+
+    def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_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)
+        self.llm.llm = llm_llm
+        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
+        self.flow.encoder = flow_encoder
+
+    def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent, fp16):
+        assert torch.cuda.is_available(), 'tensorrt only supports gpu!'
+        if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0:
+            convert_onnx_to_trt(flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16)
+        del self.flow.decoder.estimator
+        import tensorrt as trt
+        with open(flow_decoder_estimator_model, 'rb') as f:
+            estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
+        assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model)
+        self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device)
+
+    def get_trt_kwargs(self):
+        min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)]
+        opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)]
+        max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)]
+        input_names = ["x", "mask", "mu", "cond"]
+        return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
+
+    def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
+        cur_silent_token_num, max_silent_token_num = 0, 5
+        try:
+            with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False):
+                if isinstance(text, Generator):
+                    assert (self.__class__.__name__ != 'CosyVoiceModel') and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2/3 and do not support vllm!'
+                    token_generator = self.llm.inference_bistream(text=text,
+                                                                  prompt_text=prompt_text.to(self.device),
+                                                                  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))
+                else:
+                    token_generator = self.llm.inference(text=text.to(self.device),
+                                                         text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
+                                                         prompt_text=prompt_text.to(self.device),
+                                                         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),
+                                                         uuid=uuid)
+                for i in token_generator:
+                    if i in self.silent_tokens:
+                        cur_silent_token_num += 1
+                        if cur_silent_token_num > max_silent_token_num:
+                            continue
+                    else:
+                        cur_silent_token_num = 0
+                    self.tts_speech_token_dict[uuid].append(i)
+        except Exception as exc:
+            self.llm_exception_dict[uuid] = exc
+        finally:
+            self.llm_end_dict[uuid] = True
+
+    def vc_job(self, source_speech_token, uuid):
+        self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist()
+        self.llm_end_dict[uuid] = True
+
+    def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
+        with torch.cuda.amp.autocast(self.fp16):
+            tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device, dtype=torch.int32),
+                                                                      token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+                                                                      prompt_token=prompt_token.to(self.device),
+                                                                      prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+                                                                      prompt_feat=prompt_feat.to(self.device),
+                                                                      prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+                                                                      embedding=embedding.to(self.device),
+                                                                      flow_cache=self.flow_cache_dict[uuid])
+
+        # mel overlap fade in out
+        if self.mel_overlap_dict[uuid].shape[2] != 0:
+            tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
+        # append hift cache
+        if self.hift_cache_dict[uuid] is not None:
+            hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
+            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
+        else:
+            hift_cache_source = torch.zeros(1, 1, 0)
+        # keep overlap mel and hift cache
+        if finalize is False:
+            self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
+            tts_mel = tts_mel[:, :, :-self.mel_overlap_len]
+            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)
+            self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
+                                          'source': tts_source[:, :, -self.source_cache_len:],
+                                          'speech': tts_speech[:, -self.source_cache_len:]}
+            tts_speech = tts_speech[:, :-self.source_cache_len]
+        else:
+            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(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
+
+    def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
+            prompt_text=torch.zeros(1, 0, dtype=torch.int32),
+            llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
+            flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
+            prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
+        # this_uuid is used to track variables related to this inference thread
+        this_uuid = str(uuid.uuid1())
+        with self.lock:
+            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
+            self.hift_cache_dict[this_uuid] = None
+            self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
+            self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
+        if source_speech_token.shape[1] == 0:
+            p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
+        else:
+            p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
+        p.start()
+        if stream is True:
+            token_hop_len = self.token_min_hop_len
+            while True:
+                time.sleep(0.1)
+                if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
+                    this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \
+                        .unsqueeze(dim=0)
+                    this_tts_speech = self.token2wav(token=this_tts_speech_token,
+                                                     prompt_token=flow_prompt_speech_token,
+                                                     prompt_feat=prompt_speech_feat,
+                                                     embedding=flow_embedding,
+                                                     uuid=this_uuid,
+                                                     finalize=False)
+                    yield {'tts_speech': this_tts_speech.cpu()}
+                    with self.lock:
+                        self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
+                    # increase token_hop_len for better speech quality
+                    token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
+                if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len:
+                    break
+            p.join()
+            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
+            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
+            this_tts_speech = self.token2wav(token=this_tts_speech_token,
+                                             prompt_token=flow_prompt_speech_token,
+                                             prompt_feat=prompt_speech_feat,
+                                             embedding=flow_embedding,
+                                             uuid=this_uuid,
+                                             finalize=True)
+            yield {'tts_speech': this_tts_speech.cpu()}
+        else:
+            # deal with all tokens
+            p.join()
+            if this_uuid in self.llm_exception_dict:
+                raise self.llm_exception_dict[this_uuid]
+            if len(self.tts_speech_token_dict[this_uuid]) == 0:
+                raise RuntimeError('CosyVoice LLM produced no speech tokens.')
+            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
+            this_tts_speech = self.token2wav(token=this_tts_speech_token,
+                                             prompt_token=flow_prompt_speech_token,
+                                             prompt_feat=prompt_speech_feat,
+                                             embedding=flow_embedding,
+                                             uuid=this_uuid,
+                                             finalize=True,
+                                             speed=speed)
+            yield {'tts_speech': this_tts_speech.cpu()}
+        with self.lock:
+            self.tts_speech_token_dict.pop(this_uuid)
+            self.llm_end_dict.pop(this_uuid)
+            self.llm_exception_dict.pop(this_uuid, None)
+            self.mel_overlap_dict.pop(this_uuid)
+            self.hift_cache_dict.pop(this_uuid)
+            self.flow_cache_dict.pop(this_uuid)
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.current_stream().synchronize()
+
+
+class CosyVoice2Model(CosyVoiceModel):
+
+    def __init__(self,
+                 llm: torch.nn.Module,
+                 flow: torch.nn.Module,
+                 hift: torch.nn.Module,
+                 fp16: bool = False):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.llm = llm
+        self.flow = flow
+        self.hift = hift
+        self.fp16 = fp16
+        # NOTE must matching training static_chunk_size
+        self.token_hop_len = 25
+        # NOTE increase token_hop_len incrementally to avoid duplicate inference
+        self.token_max_hop_len = 4 * self.token_hop_len
+        self.stream_scale_factor = 2
+        assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
+        # hift cache
+        self.mel_cache_len = 8
+        self.source_cache_len = int(self.mel_cache_len * 480)
+        # speech fade in out
+        self.speech_window = np.hamming(2 * self.source_cache_len)
+        # rtf and decoding related
+        self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
+        self.lock = threading.Lock()
+        # dict used to store session related variable
+        self.tts_speech_token_dict = {}
+        self.llm_end_dict = {}
+        self.hift_cache_dict = {}
+        self.llm_exception_dict = {}
+        self.silent_tokens = []
+
+    def load_jit(self, flow_encoder_model):
+        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
+        self.flow.encoder = flow_encoder
+
+    def load_vllm(self, model_dir):
+        export_cosyvoice2_vllm(self.llm, model_dir, self.device)
+        from vllm import EngineArgs, LLMEngine
+        engine_args = EngineArgs(model=model_dir,
+                                 skip_tokenizer_init=True,
+                                 enable_prompt_embeds=True,
+                                 gpu_memory_utilization=0.2)
+        self.llm.vllm = LLMEngine.from_engine_args(engine_args)
+        self.llm.lock = threading.Lock()
+        del self.llm.llm.model.model.layers
+
+    def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0):
+        with torch.cuda.amp.autocast(self.fp16):
+            tts_mel, _ = self.flow.inference(token=token.to(self.device, dtype=torch.int32),
+                                             token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+                                             prompt_token=prompt_token.to(self.device),
+                                             prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+                                             prompt_feat=prompt_feat.to(self.device),
+                                             prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+                                             embedding=embedding.to(self.device),
+                                             streaming=stream,
+                                             finalize=finalize)
+        tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
+        # append hift cache
+        if self.hift_cache_dict[uuid] is not None:
+            hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
+            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
+        else:
+            hift_cache_source = torch.zeros(1, 1, 0)
+        # keep overlap mel and hift cache
+        if finalize is False:
+            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)
+            self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
+                                          'source': tts_source[:, :, -self.source_cache_len:],
+                                          'speech': tts_speech[:, -self.source_cache_len:]}
+            tts_speech = tts_speech[:, :-self.source_cache_len]
+        else:
+            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(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
+
+    def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
+            prompt_text=torch.zeros(1, 0, dtype=torch.int32),
+            llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
+            flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
+            prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
+        # this_uuid is used to track variables related to this inference thread
+        this_uuid = str(uuid.uuid1())
+        with self.lock:
+            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
+            self.hift_cache_dict[this_uuid] = None
+        if source_speech_token.shape[1] == 0:
+            p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
+        else:
+            p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
+        p.start()
+        if stream is True:
+            token_offset = 0
+            prompt_token_pad = int(np.ceil(flow_prompt_speech_token.shape[1] / self.token_hop_len) * self.token_hop_len - flow_prompt_speech_token.shape[1])
+            while True:
+                time.sleep(0.1)
+                this_token_hop_len = self.token_hop_len + prompt_token_pad if token_offset == 0 else self.token_hop_len
+                if len(self.tts_speech_token_dict[this_uuid]) - token_offset >= this_token_hop_len + self.flow.pre_lookahead_len:
+                    this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_offset + this_token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0)
+                    this_tts_speech = self.token2wav(token=this_tts_speech_token,
+                                                     prompt_token=flow_prompt_speech_token,
+                                                     prompt_feat=prompt_speech_feat,
+                                                     embedding=flow_embedding,
+                                                     token_offset=token_offset,
+                                                     uuid=this_uuid,
+                                                     stream=stream,
+                                                     finalize=False)
+                    token_offset += this_token_hop_len
+                    self.token_hop_len = min(self.token_max_hop_len, self.token_hop_len * self.stream_scale_factor)
+                    yield {'tts_speech': this_tts_speech.cpu()}
+                if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) - token_offset < this_token_hop_len + self.flow.pre_lookahead_len:
+                    break
+            p.join()
+            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
+            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
+            this_tts_speech = self.token2wav(token=this_tts_speech_token,
+                                             prompt_token=flow_prompt_speech_token,
+                                             prompt_feat=prompt_speech_feat,
+                                             embedding=flow_embedding,
+                                             token_offset=token_offset,
+                                             uuid=this_uuid,
+                                             finalize=True)
+            yield {'tts_speech': this_tts_speech.cpu()}
+        else:
+            # deal with all tokens
+            p.join()
+            if this_uuid in self.llm_exception_dict:
+                raise self.llm_exception_dict[this_uuid]
+            if len(self.tts_speech_token_dict[this_uuid]) == 0:
+                raise RuntimeError('CosyVoice LLM produced no speech tokens.')
+            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
+            this_tts_speech = self.token2wav(token=this_tts_speech_token,
+                                             prompt_token=flow_prompt_speech_token,
+                                             prompt_feat=prompt_speech_feat,
+                                             embedding=flow_embedding,
+                                             token_offset=0,
+                                             uuid=this_uuid,
+                                             finalize=True,
+                                             speed=speed)
+            yield {'tts_speech': this_tts_speech.cpu()}
+        with self.lock:
+            self.tts_speech_token_dict.pop(this_uuid)
+            self.llm_end_dict.pop(this_uuid)
+            self.llm_exception_dict.pop(this_uuid, None)
+            self.hift_cache_dict.pop(this_uuid)
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.current_stream().synchronize()
+
+
+class CosyVoice3Model(CosyVoice2Model):
+
+    def __init__(self,
+                 llm: torch.nn.Module,
+                 flow: torch.nn.Module,
+                 hift: torch.nn.Module,
+                 fp16: bool = False):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.llm = llm
+        self.flow = flow
+        self.hift = hift
+        self.fp16 = fp16
+        # NOTE must matching training static_chunk_size
+        self.token_hop_len = 25
+        # NOTE increase token_hop_len incrementally to avoid duplicate inference
+        self.token_max_hop_len = 4 * self.token_hop_len
+        self.stream_scale_factor = 2
+        assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
+        # rtf and decoding related
+        self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
+        self.lock = threading.Lock()
+        # dict used to store session related variable
+        self.tts_speech_token_dict = {}
+        self.llm_end_dict = {}
+        self.hift_cache_dict = {}
+        self.llm_exception_dict = {}
+        # FSQ silent and breath token
+        self.silent_tokens = [1, 2, 28, 29, 55, 248, 494, 2241, 2242, 2322, 2323]
+
+    def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0):
+        with torch.cuda.amp.autocast(self.fp16):
+            tts_mel, _ = self.flow.inference(token=token.to(self.device, dtype=torch.int32),
+                                             token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+                                             prompt_token=prompt_token.to(self.device),
+                                             prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+                                             prompt_feat=prompt_feat.to(self.device),
+                                             prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+                                             embedding=embedding.to(self.device),
+                                             streaming=stream,
+                                             finalize=finalize)
+            tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
+            # append mel cache
+            if self.hift_cache_dict[uuid] is not None:
+                hift_cache_mel = self.hift_cache_dict[uuid]['mel']
+                tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
+                self.hift_cache_dict[uuid]['mel'] = tts_mel
+            else:
+                self.hift_cache_dict[uuid] = {'mel': tts_mel, 'speech_offset': 0}
+            if speed != 1.0:
+                assert token_offset == 0 and finalize is True, '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, _ = self.hift.inference(speech_feat=tts_mel, finalize=finalize)
+            tts_speech = tts_speech[:, self.hift_cache_dict[uuid]['speech_offset']:]
+            self.hift_cache_dict[uuid]['speech_offset'] += tts_speech.shape[1]
+        return tts_speech

third_party/CosyVoice/cosyvoice/dataset/dataset.py

@@ -0,0 +1,155 @@
+# Copyright (c) 2021 Mobvoi Inc. (authors: 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 random
+import math
+from functools import partial
+
+import torch
+import torch.distributed as dist
+from torch.utils.data import IterableDataset
+from cosyvoice.utils.file_utils import read_lists
+
+
+class Processor(IterableDataset):
+
+    def __init__(self, source, f, *args, **kw):
+        assert callable(f)
+        self.source = source
+        self.f = f
+        self.args = args
+        self.kw = kw
+
+    def set_epoch(self, epoch):
+        self.source.set_epoch(epoch)
+
+    def __iter__(self):
+        """ Return an iterator over the source dataset processed by the
+            given processor.
+        """
+        assert self.source is not None
+        assert callable(self.f)
+        return self.f(iter(self.source), *self.args, **self.kw)
+
+    def apply(self, f):
+        assert callable(f)
+        return Processor(self, f, *self.args, **self.kw)
+
+
+class DistributedSampler:
+
+    def __init__(self, shuffle=True, partition=True):
+        self.epoch = -1
+        self.update()
+        self.shuffle = shuffle
+        self.partition = partition
+
+    def update(self):
+        assert dist.is_available()
+        if dist.is_initialized():
+            self.rank = dist.get_rank()
+            self.world_size = dist.get_world_size()
+        else:
+            self.rank = 0
+            self.world_size = 1
+        worker_info = torch.utils.data.get_worker_info()
+        if worker_info is None:
+            self.worker_id = 0
+            self.num_workers = 1
+        else:
+            self.worker_id = worker_info.id
+            self.num_workers = worker_info.num_workers
+        return dict(rank=self.rank,
+                    world_size=self.world_size,
+                    worker_id=self.worker_id,
+                    num_workers=self.num_workers)
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
+
+    def sample(self, data):
+        """ Sample data according to rank/world_size/num_workers
+
+            Args:
+                data(List): input data list
+
+            Returns:
+                List: data list after sample
+        """
+        data = list(range(len(data)))
+        # force datalist even
+        if self.partition:
+            if self.shuffle:
+                random.Random(self.epoch).shuffle(data)
+            if len(data) < self.world_size:
+                data = data * math.ceil(self.world_size / len(data))
+                data = data[:self.world_size]
+            data = data[self.rank::self.world_size]
+        if len(data) < self.num_workers:
+            data = data * math.ceil(self.num_workers / len(data))
+            data = data[:self.num_workers]
+        data = data[self.worker_id::self.num_workers]
+        return data
+
+
+class DataList(IterableDataset):
+
+    def __init__(self, lists, shuffle=True, partition=True):
+        self.lists = lists
+        self.sampler = DistributedSampler(shuffle, partition)
+
+    def set_epoch(self, epoch):
+        self.sampler.set_epoch(epoch)
+
+    def __iter__(self):
+        sampler_info = self.sampler.update()
+        indexes = self.sampler.sample(self.lists)
+        for index in indexes:
+            data = dict(src=self.lists[index])
+            data.update(sampler_info)
+            yield data
+
+
+def Dataset(data_list_file,
+            data_pipeline,
+            mode='train',
+            gan=False,
+            dpo=False,
+            shuffle=True,
+            partition=True):
+    """ Construct dataset from arguments
+
+        We have two shuffle stage in the Dataset. The first is global
+        shuffle at shards tar/raw file level. The second is global shuffle
+        at training samples level.
+
+        Args:
+            data_type(str): raw/shard
+            tokenizer (BaseTokenizer): tokenizer to tokenize
+            partition(bool): whether to do data partition in terms of rank
+    """
+    lists = read_lists(data_list_file)
+    dataset = DataList(lists,
+                       shuffle=shuffle,
+                       partition=partition)
+    # map partial arg to padding func
+    for i in range(1, len(data_pipeline)):
+        if data_pipeline[i].func.__name__ == 'compute_fbank' and gan is True:
+            data_pipeline[i] = partial(data_pipeline[i], token_mel_ratio=0)
+        if data_pipeline[i].func.__name__ == 'padding':
+            data_pipeline[i] = partial(data_pipeline[i], gan=gan, dpo=dpo)
+    for func in data_pipeline:
+        dataset = Processor(dataset, func, mode=mode)
+    return dataset

third_party/CosyVoice/cosyvoice/dataset/processor.py

@@ -0,0 +1,431 @@
+# 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 logging
+import random
+
+import pyarrow.parquet as pq
+from io import BytesIO
+import numpy as np
+import whisper
+import torch
+import torchaudio
+from torch.nn.utils.rnn import pad_sequence
+import torch.nn.functional as F
+import pyworld as pw
+from cosyvoice.utils.onnx import embedding_extractor, online_feature
+
+AUDIO_FORMAT_SETS = {'flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma'}
+
+
+def parquet_opener(data, mode='train'):
+    """ Give url or local file, return file descriptor
+        Inplace operation.
+
+        Args:
+            data(Iterable[str]): url or local file list
+
+        Returns:
+            Iterable[{src, stream}]
+    """
+    for sample in data:
+        assert 'src' in sample
+        url = sample['src']
+        try:
+            for df in pq.ParquetFile(url).iter_batches(batch_size=64):
+                df = df.to_pandas()
+                for i in range(len(df)):
+                    sample.update(dict(df.loc[i]))
+                    # NOTE do not return sample directly, must initialize a new dict
+                    yield {**sample}
+        except Exception as ex:
+            logging.warning('Failed to open {}, ex info {}'.format(url, ex))
+
+
+def filter(data,
+           max_length=10240,
+           min_length=10,
+           token_max_length=200,
+           token_min_length=1,
+           min_output_input_ratio=0.0005,
+           max_output_input_ratio=1,
+           mode='train'):
+    """ Filter sample according to feature and label length
+        Inplace operation.
+
+        Args::
+            data: Iterable[{key, wav, label, sample_rate}]
+            max_length: drop utterance which is greater than max_length(10ms)
+            min_length: drop utterance which is less than min_length(10ms)
+            token_max_length: drop utterance which is greater than
+                token_max_length, especially when use char unit for
+                english modeling
+            token_min_length: drop utterance which is
+                less than token_max_length
+            min_output_input_ratio: minimal ration of
+                token_length / feats_length(10ms)
+            max_output_input_ratio: maximum ration of
+                token_length / feats_length(10ms)
+
+        Returns:
+            Iterable[{key, wav, label, sample_rate}]
+    """
+    for sample in data:
+        sample['speech'], sample['sample_rate'] = torchaudio.load(BytesIO(sample['audio_data']))
+        sample['speech'] = sample['speech'].mean(dim=0, keepdim=True)
+        del sample['audio_data']
+        # sample['wav'] is torch.Tensor, we have 100 frames every second
+        num_frames = sample['speech'].size(1) / sample['sample_rate'] * 100
+        if num_frames < min_length:
+            continue
+        if num_frames > max_length:
+            continue
+        if len(sample['text_token']) < token_min_length:
+            continue
+        if len(sample['text_token']) > token_max_length:
+            continue
+        if online_feature is False and len(sample['speech_token']) == 0:
+            continue
+        if online_feature is False and 'reject_speech_token' in sample and len(sample['reject_speech_token']) == 0:
+            continue
+        if num_frames != 0:
+            if len(sample['text_token']) / num_frames < min_output_input_ratio:
+                continue
+            if len(sample['text_token']) / num_frames > max_output_input_ratio:
+                continue
+        yield sample
+
+
+def resample(data, resample_rate=22050, min_sample_rate=16000, mode='train'):
+    """ Resample data.
+        Inplace operation.
+
+        Args:
+            data: Iterable[{key, wav, label, sample_rate}]
+            resample_rate: target resample rate
+
+        Returns:
+            Iterable[{key, wav, label, sample_rate}]
+    """
+    for sample in data:
+        assert 'sample_rate' in sample
+        assert 'speech' in sample
+        sample_rate = sample['sample_rate']
+        waveform = sample['speech']
+        if sample_rate != resample_rate:
+            if sample_rate < min_sample_rate:
+                continue
+            sample['sample_rate'] = resample_rate
+            sample['speech'] = torchaudio.transforms.Resample(
+                orig_freq=sample_rate, new_freq=resample_rate)(waveform)
+        max_val = sample['speech'].abs().max()
+        if max_val > 1:
+            sample['speech'] /= max_val
+        yield sample
+
+
+def truncate(data, truncate_length=24576, mode='train'):
+    """ Truncate data.
+
+        Args:
+            data: Iterable[{key, wav, label, sample_rate}]
+            truncate_length: truncate length
+
+        Returns:
+            Iterable[{key, wav, label, sample_rate}]
+    """
+    for sample in data:
+        waveform = sample['speech']
+        if waveform.shape[1] > truncate_length:
+            start = random.randint(0, waveform.shape[1] - truncate_length)
+            waveform = waveform[:, start: start + truncate_length]
+        else:
+            waveform = torch.concat([waveform, torch.zeros(1, truncate_length - waveform.shape[1])], dim=1)
+        sample['speech'] = waveform
+        yield sample
+
+
+def compute_fbank(data,
+                  feat_extractor,
+                  num_frames=-1,
+                  mode='train'):
+    """ Extract fbank
+
+        Args:
+            data: Iterable[{key, wav, label, sample_rate}]
+
+        Returns:
+            Iterable[{key, feat, label}]
+    """
+    for sample in data:
+        assert 'sample_rate' in sample
+        assert 'speech' in sample
+        assert 'utt' in sample
+        assert 'text_token' in sample
+        # NOTE in cosyvoice2/3, we support online token extraction, so we need to align speech to 25hz first
+        if num_frames != -1:
+            index = int(np.ceil(sample['speech'].shape[1] / num_frames))
+            sample['speech'] = torch.concat([sample['speech'], torch.zeros(1, index * num_frames - sample['speech'].shape[1])], dim=1)
+        sample['speech_feat'] = feat_extractor(sample['speech']).squeeze(dim=0).transpose(0, 1)
+        yield sample
+
+
+def compute_whisper_fbank(data, num_frames=-1, mode='train'):
+    """ Extract whisper fbank
+
+        Args:
+            data: Iterable[{key, wav, label, sample_rate}]
+
+        Returns:
+            Iterable[{key, feat, label}]
+    """
+    for sample in data:
+        if num_frames != -1:
+            assert sample['speech'].shape[1] % num_frames == 0, 'speech length is not aligned with speech_token'
+        sample['speech_16k'] = torchaudio.transforms.Resample(orig_freq=sample['sample_rate'], new_freq=16000)(sample['speech'])
+        sample['whisper_feat'] = whisper.log_mel_spectrogram(sample['speech_16k'], n_mels=128).squeeze(dim=0).transpose(0, 1)
+        yield sample
+
+
+def compute_f0(data, sample_rate, hop_size, mode='train'):
+    """ Extract f0
+
+        Args:
+            data: Iterable[{key, wav, label, sample_rate}]
+
+        Returns:
+            Iterable[{key, feat, label}]
+    """
+    frame_period = hop_size * 1000 / sample_rate
+    for sample in data:
+        assert 'sample_rate' in sample
+        assert 'speech' in sample
+        assert 'utt' in sample
+        assert 'text_token' in sample
+        waveform = sample['speech']
+        _f0, t = pw.harvest(waveform.squeeze(dim=0).numpy().astype('double'), sample_rate, frame_period=frame_period)
+        if sum(_f0 != 0) < 5:  # this happens when the algorithm fails
+            _f0, t = pw.dio(waveform.squeeze(dim=0).numpy().astype('double'), sample_rate, frame_period=frame_period)  # if harvest fails, try dio
+        f0 = pw.stonemask(waveform.squeeze(dim=0).numpy().astype('double'), _f0, t, sample_rate)
+        f0 = F.interpolate(torch.from_numpy(f0).view(1, 1, -1), size=sample['speech_feat'].shape[0], mode='linear').view(-1)
+        sample['pitch_feat'] = f0
+        yield sample
+
+
+def parse_embedding(data, normalize, mode='train'):
+    """ Parse utt_embedding/spk_embedding
+
+        Args:
+            data: Iterable[{key, wav, label, sample_rate}]
+
+        Returns:
+            Iterable[{key, feat, label}]
+    """
+    for sample in data:
+        if 'utt_embedding' not in sample and 'spk_embedding' not in sample:
+            sample['speech_16k'] = torchaudio.transforms.Resample(orig_freq=sample['sample_rate'], new_freq=16000)(sample['speech'])
+            embedding = embedding_extractor.inference(sample['speech_16k'])
+            sample['spk_embedding'] = sample['utt_embedding'] = embedding
+        else:
+            sample['utt_embedding'] = torch.tensor(sample['utt_embedding'], dtype=torch.float32)
+            sample['spk_embedding'] = torch.tensor(sample['spk_embedding'], dtype=torch.float32)
+        if normalize:
+            sample['utt_embedding'] = F.normalize(sample['utt_embedding'], dim=0)
+            sample['spk_embedding'] = F.normalize(sample['spk_embedding'], dim=0)
+        yield sample
+
+
+def tokenize(data, get_tokenizer, allowed_special, mode='train'):
+    """ Decode text to chars or BPE
+        Inplace operation
+
+        Args:
+            data: Iterable[{key, wav, txt, sample_rate}]
+
+        Returns:
+            Iterable[{key, wav, txt, tokens, label, sample_rate}]
+    """
+    tokenizer = get_tokenizer()
+    for sample in data:
+        assert 'text' in sample
+        sample['text_token'] = tokenizer.encode(sample['text'], allowed_special=allowed_special)
+        if 'instruct' in sample:
+            sample['instruct_token'] = tokenizer.encode(sample['instruct'], allowed_special=allowed_special)
+        yield sample
+
+
+def shuffle(data, shuffle_size=10000, mode='train'):
+    """ Local shuffle the data
+
+        Args:
+            data: Iterable[{key, feat, label}]
+            shuffle_size: buffer size for shuffle
+
+        Returns:
+            Iterable[{key, feat, label}]
+    """
+    buf = []
+    yield_size = int(shuffle_size / 2)
+    for sample in data:
+        buf.append(sample)
+        if len(buf) >= shuffle_size:
+            random.shuffle(buf)
+            for x in buf[:yield_size]:
+                yield x
+            buf = buf[yield_size:]
+    # The sample left over
+    random.shuffle(buf)
+    for x in buf:
+        yield x
+
+
+def sort(data, sort_size=500, mode='train'):
+    """ Sort the data by feature length.
+        Sort is used after shuffle and before batch, so we can group
+        utts with similar lengths into a batch, and `sort_size` should
+        be less than `shuffle_size`
+
+        Args:
+            data: Iterable[{key, feat, label}]
+            sort_size: buffer size for sort
+
+        Returns:
+            Iterable[{key, feat, label}]
+    """
+
+    buf = []
+    for sample in data:
+        buf.append(sample)
+        if len(buf) >= sort_size:
+            buf.sort(key=lambda x: x['speech_feat'].size(0))
+            for x in buf:
+                yield x
+            buf = []
+    # The sample left over
+    buf.sort(key=lambda x: x['speech_feat'].size(0))
+    for x in buf:
+        yield x
+
+
+def static_batch(data, batch_size=16):
+    """ Static batch the data by `batch_size`
+
+        Args:
+            data: Iterable[{key, feat, label}]
+            batch_size: batch size
+
+        Returns:
+            Iterable[List[{key, feat, label}]]
+    """
+    buf = []
+    for sample in data:
+        buf.append(sample)
+        if len(buf) >= batch_size:
+            yield buf
+            buf = []
+    if len(buf) > 0:
+        yield buf
+
+
+def dynamic_batch(data, max_frames_in_batch=12000, mode='train'):
+    """ Dynamic batch the data until the total frames in batch
+        reach `max_frames_in_batch`
+
+        Args:
+            data: Iterable[{key, feat, label}]
+            max_frames_in_batch: max_frames in one batch
+
+        Returns:
+            Iterable[List[{key, feat, label}]]
+    """
+    buf = []
+    longest_frames = 0
+    for sample in data:
+        assert 'speech_feat' in sample
+        assert isinstance(sample['speech_feat'], torch.Tensor)
+        new_sample_frames = sample['speech_feat'].size(0)
+        longest_frames = max(longest_frames, new_sample_frames)
+        frames_after_padding = longest_frames * (len(buf) + 1)
+        if frames_after_padding > max_frames_in_batch:
+            yield buf
+            buf = [sample]
+            longest_frames = new_sample_frames
+        else:
+            buf.append(sample)
+    if len(buf) > 0:
+        yield buf
+
+
+def batch(data, batch_type='static', batch_size=16, max_frames_in_batch=12000, mode='train'):
+    """ Wrapper for static/dynamic batch
+    """
+    if batch_type == 'static':
+        return static_batch(data, batch_size)
+    elif batch_type == 'dynamic':
+        return dynamic_batch(data, max_frames_in_batch)
+    else:
+        logging.fatal('Unsupported batch type {}'.format(batch_type))
+
+
+def padding(data, use_spk_embedding, mode='train', gan=False, dpo=False):
+    """ Padding the data into training data
+
+        Args:
+            data: Iterable[List[{key, feat, label}]]
+
+        Returns:
+            Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)]
+    """
+    for sample in data:
+        assert isinstance(sample, list)
+        order = torch.argsort(torch.tensor([x['speech'].size(1) for x in sample], dtype=torch.int32), descending=True)
+        batch = {}
+        batch['utts'] = [sample[i]['utt'] for i in order]
+        batch['text'] = [sample[i]['text'] for i in order]
+        text_token = [torch.tensor(sample[i]['text_token']) for i in order]
+        batch['text_token_len'] = torch.tensor([i.size(0) for i in text_token], dtype=torch.int32)
+        batch['text_token'] = pad_sequence(text_token, batch_first=True, padding_value=0)
+        speech_feat = [sample[i]['speech_feat'] for i in order]
+        batch['speech_feat_len'] = torch.tensor([i.size(0) for i in speech_feat], dtype=torch.int32)
+        batch['speech_feat'] = pad_sequence(speech_feat, batch_first=True, padding_value=0)
+        batch['utt_embedding'] = torch.stack([sample[i]['utt_embedding'] for i in order], dim=0)
+        batch['spk_embedding'] = torch.stack([sample[i]['spk_embedding'] for i in order], dim=0)
+        if torch.tensor(['instruct_token' in sample[i] for i in order]).all():
+            instruct_token = [torch.tensor(sample[i]['instruct_token']) for i in order]
+            batch['instruct_token_len'] = torch.tensor([i.size(0) for i in instruct_token], dtype=torch.int32)
+            batch['instruct_token'] = pad_sequence(instruct_token, batch_first=True, padding_value=0)
+        if torch.tensor(['whisper_feat' in sample[i] for i in order]).all():
+            whisper_feat = [sample[i]['whisper_feat'] for i in order]
+            batch['whisper_feat_len'] = torch.tensor([i.size(0) for i in whisper_feat], dtype=torch.int32)
+            batch['whisper_feat'] = pad_sequence(whisper_feat, batch_first=True, padding_value=0)
+        if torch.tensor(['speech_token' in sample[i] for i in order]).all():
+            speech_token = [torch.tensor(sample[i]['speech_token']) for i in order]
+            batch['speech_token_len'] = torch.tensor([i.size(0) for i in speech_token], dtype=torch.int32)
+            batch['speech_token'] = pad_sequence(speech_token, batch_first=True, padding_value=0)
+        if gan is True:
+            # in gan train, we need speech/pitch_feat
+            speech = [sample[i]['speech'].squeeze(dim=0) for i in order]
+            batch['speech_len'] = torch.tensor([i.size(0) for i in speech], dtype=torch.int32)
+            batch['speech'] = pad_sequence(speech, batch_first=True, padding_value=0)
+            pitch_feat = [sample[i]['pitch_feat'] for i in order]
+            batch['pitch_feat_len'] = torch.tensor([i.size(0) for i in pitch_feat], dtype=torch.int32)
+            batch['pitch_feat'] = pad_sequence(pitch_feat, batch_first=True, padding_value=0)
+        if dpo is True:
+            reject_speech_token = [torch.tensor(sample[i]['reject_speech_token']) for i in order]
+            batch['reject_speech_token_len'] = torch.tensor([i.size(0) for i in reject_speech_token], dtype=torch.int32)
+            batch['reject_speech_token'] = pad_sequence(reject_speech_token, batch_first=True, padding_value=0)
+        if use_spk_embedding is True:
+            batch["embedding"] = batch["spk_embedding"]
+        else:
+            batch["embedding"] = batch["utt_embedding"]
+        yield batch

third_party/CosyVoice/cosyvoice/flow/DiT/dit.py

@@ -0,0 +1,176 @@
+
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from einops import repeat
+from x_transformers.x_transformers import RotaryEmbedding
+from cosyvoice.utils.mask import add_optional_chunk_mask
+from cosyvoice.flow.DiT.modules import (
+    TimestepEmbedding,
+    ConvNeXtV2Block,
+    CausalConvPositionEmbedding,
+    DiTBlock,
+    AdaLayerNormZero_Final,
+    precompute_freqs_cis,
+    get_pos_embed_indices,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        batch, text_len = text.shape[0], text.shape[1]
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim, spk_dim=None):
+        super().__init__()
+        spk_dim = 0 if spk_dim is None else spk_dim
+        self.spk_dim = spk_dim
+        self.proj = nn.Linear(mel_dim * 2 + text_dim + spk_dim, out_dim)
+        self.conv_pos_embed = CausalConvPositionEmbedding(dim=out_dim)
+
+    def forward(
+            self,
+            x: float["b n d"],
+            cond: float["b n d"],
+            text_embed: float["b n d"],
+            spks: float["b d"],
+    ):
+        to_cat = [x, cond, text_embed]
+        if self.spk_dim > 0:
+            spks = repeat(spks, "b c -> b t c", t=x.shape[1])
+            to_cat.append(spks)
+
+        x = self.proj(torch.cat(to_cat, dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using DiT blocks
+
+
+class DiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=80,
+        mu_dim=None,
+        long_skip_connection=False,
+        spk_dim=None,
+        out_channels=None,
+        static_chunk_size=50,
+        num_decoding_left_chunks=2
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        if mu_dim is None:
+            mu_dim = mel_dim
+        self.input_embed = InputEmbedding(mel_dim, mu_dim, dim, spk_dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)]
+        )
+        self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
+
+        self.norm_out = AdaLayerNormZero_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+        self.out_channels = out_channels
+        self.static_chunk_size = static_chunk_size
+        self.num_decoding_left_chunks = num_decoding_left_chunks
+
+    def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
+        x = x.transpose(1, 2)
+        mu = mu.transpose(1, 2)
+        cond = cond.transpose(1, 2)
+        spks = spks.unsqueeze(dim=1)
+        batch, seq_len = x.shape[0], x.shape[1]
+        if t.ndim == 0:
+            t = t.repeat(batch)
+
+        # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(t)
+        x = self.input_embed(x, cond, mu, spks.squeeze(1))
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len)
+
+        if self.long_skip_connection is not None:
+            residual = x
+
+        if streaming is True:
+            attn_mask = add_optional_chunk_mask(x, mask.bool(), False, False, 0, self.static_chunk_size, -1).unsqueeze(dim=1)
+        else:
+            attn_mask = add_optional_chunk_mask(x, mask.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1).unsqueeze(dim=1)
+
+        for block in self.transformer_blocks:
+            x = block(x, t, mask=attn_mask.bool(), rope=rope)
+
+        if self.long_skip_connection is not None:
+            x = self.long_skip_connection(torch.cat((x, residual), dim=-1))
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x).transpose(1, 2)
+        return output

third_party/CosyVoice/cosyvoice/flow/DiT/modules.py

@@ -0,0 +1,616 @@
+
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+from typing import Optional
+import math
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torchaudio
+
+from x_transformers.x_transformers import apply_rotary_pos_emb
+
+
+# raw wav to mel spec
+class MelSpec(nn.Module):
+    def __init__(
+        self,
+        filter_length=1024,
+        hop_length=256,
+        win_length=1024,
+        n_mel_channels=100,
+        target_sample_rate=24_000,
+        normalize=False,
+        power=1,
+        norm=None,
+        center=True,
+    ):
+        super().__init__()
+        self.n_mel_channels = n_mel_channels
+
+        self.mel_stft = torchaudio.transforms.MelSpectrogram(
+            sample_rate=target_sample_rate,
+            n_fft=filter_length,
+            win_length=win_length,
+            hop_length=hop_length,
+            n_mels=n_mel_channels,
+            power=power,
+            center=center,
+            normalized=normalize,
+            norm=norm,
+        )
+
+        self.register_buffer("dummy", torch.tensor(0), persistent=False)
+
+    def forward(self, inp):
+        if len(inp.shape) == 3:
+            inp = inp.squeeze(1)  # 'b 1 nw -> b nw'
+
+        assert len(inp.shape) == 2
+
+        if self.dummy.device != inp.device:
+            self.to(inp.device)
+
+        mel = self.mel_stft(inp)
+        mel = mel.clamp(min=1e-5).log()
+        return mel
+
+
+# sinusoidal position embedding
+
+
+class SinusPositionEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x, scale=1000):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+# convolutional position embedding
+
+
+class ConvPositionEmbedding(nn.Module):
+    def __init__(self, dim, kernel_size=31, groups=16):
+        super().__init__()
+        assert kernel_size % 2 != 0
+        self.conv1d = nn.Sequential(
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+        )
+
+    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):  # noqa: F722
+        if mask is not None:
+            mask = mask[..., None]
+            x = x.masked_fill(~mask, 0.0)
+
+        x = x.permute(0, 2, 1)
+        x = self.conv1d(x)
+        out = x.permute(0, 2, 1)
+
+        if mask is not None:
+            out = out.masked_fill(~mask, 0.0)
+
+        return out
+
+
+class CausalConvPositionEmbedding(nn.Module):
+    def __init__(self, dim, kernel_size=31, groups=16):
+        super().__init__()
+        assert kernel_size % 2 != 0
+        self.kernel_size = kernel_size
+        self.conv1 = nn.Sequential(
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=0),
+            nn.Mish(),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=0),
+            nn.Mish(),
+        )
+
+    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):  # noqa: F722
+        if mask is not None:
+            mask = mask[..., None]
+            x = x.masked_fill(~mask, 0.0)
+
+        x = x.permute(0, 2, 1)
+        x = F.pad(x, (self.kernel_size - 1, 0, 0, 0))
+        x = self.conv1(x)
+        x = F.pad(x, (self.kernel_size - 1, 0, 0, 0))
+        x = self.conv2(x)
+        out = x.permute(0, 2, 1)
+
+        if mask is not None:
+            out = out.masked_fill(~mask, 0.0)
+
+        return out
+
+
+# rotary positional embedding related
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
+    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+    # has some connection to NTK literature
+    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
+    theta *= theta_rescale_factor ** (dim / (dim - 2))
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cos = torch.cos(freqs)  # real part
+    freqs_sin = torch.sin(freqs)  # imaginary part
+    return torch.cat([freqs_cos, freqs_sin], dim=-1)
+
+
+def get_pos_embed_indices(start, length, max_pos, scale=1.0):
+    # length = length if isinstance(length, int) else length.max()
+    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
+    pos = (
+        start.unsqueeze(1)
+        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
+    )
+    # avoid extra long error.
+    pos = torch.where(pos < max_pos, pos, max_pos - 1)
+    return pos
+
+
+# Global Response Normalization layer (Instance Normalization ?)
+
+
+class GRN(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
+# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108
+
+
+class ConvNeXtV2Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        padding = (dilation * (7 - 1)) // 2
+        self.dwconv = nn.Conv1d(
+            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
+        )  # depthwise conv
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(intermediate_dim)
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = x.transpose(1, 2)  # b n d -> b d n
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # b d n -> b n d
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        return residual + x
+
+
+# AdaLayerNormZero
+# return with modulated x for attn input, and params for later mlp modulation
+
+
+class AdaLayerNormZero(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 6)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb=None):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)
+
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+# AdaLayerNormZero for final layer
+# return only with modulated x for attn input, cuz no more mlp modulation
+
+
+class AdaLayerNormZero_Final(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 2)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+
+# FeedForward
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        activation = nn.GELU(approximate=approximate)
+        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
+        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
+
+    def forward(self, x):
+        return self.ff(x)
+
+
+# Attention with possible joint part
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        processor: JointAttnProcessor | AttnProcessor,
+        dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        context_dim: Optional[int] = None,  # if not None -> joint attention
+        context_pre_only=None,
+    ):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.processor = processor
+
+        self.dim = dim
+        self.heads = heads
+        self.inner_dim = dim_head * heads
+        self.dropout = dropout
+
+        self.context_dim = context_dim
+        self.context_pre_only = context_pre_only
+
+        self.to_q = nn.Linear(dim, self.inner_dim)
+        self.to_k = nn.Linear(dim, self.inner_dim)
+        self.to_v = nn.Linear(dim, self.inner_dim)
+
+        if self.context_dim is not None:
+            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
+            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
+            if self.context_pre_only is not None:
+                self.to_q_c = nn.Linear(context_dim, self.inner_dim)
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(nn.Linear(self.inner_dim, dim))
+        self.to_out.append(nn.Dropout(dropout))
+
+        if self.context_pre_only is not None and not self.context_pre_only:
+            self.to_out_c = nn.Linear(self.inner_dim, dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        c: float["b n d"] = None,  # context c  # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.Tensor:
+        if c is not None:
+            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
+        else:
+            return self.processor(self, x, mask=mask, rope=rope)
+
+
+# Attention processor
+
+
+class AttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding
+    ) -> torch.FloatTensor:
+        batch_size = x.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # apply rotary position embedding
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+
+            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+
+        # attention
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # mask. e.g. inference got a batch with different target durations, mask out the padding
+        if mask is not None:
+            attn_mask = mask
+            if attn_mask.dim() == 2:
+                attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+                attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+        else:
+            attn_mask = None
+
+        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        x = x.to(query.dtype)
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+
+        if mask is not None:
+            if mask.dim() == 2:
+                mask = mask.unsqueeze(-1)
+            else:
+                mask = mask[:, 0, -1].unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+
+        return x
+
+
+# Joint Attention processor for MM-DiT
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class JointAttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        c: float["b nt d"] = None,  # context c, here text # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.FloatTensor:
+        residual = x
+
+        batch_size = c.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # `context` projections.
+        c_query = attn.to_q_c(c)
+        c_key = attn.to_k_c(c)
+        c_value = attn.to_v_c(c)
+
+        # apply rope for context and noised input independently
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+        if c_rope is not None:
+            freqs, xpos_scale = c_rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
+            c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)
+
+        # attention
+        query = torch.cat([query, c_query], dim=1)
+        key = torch.cat([key, c_key], dim=1)
+        value = torch.cat([value, c_value], dim=1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # mask. e.g. inference got a batch with different target durations, mask out the padding
+        if mask is not None:
+            attn_mask = F.pad(mask, (0, c.shape[1]), value=True)  # no mask for c (text)
+            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+        else:
+            attn_mask = None
+
+        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        x = x.to(query.dtype)
+
+        # Split the attention outputs.
+        x, c = (
+            x[:, : residual.shape[1]],
+            x[:, residual.shape[1]:],
+        )
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+        if not attn.context_pre_only:
+            c = attn.to_out_c(c)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)
+
+        return x, c
+
+
+# DiT Block
+
+
+class DiTBlock(nn.Module):
+    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1):
+        super().__init__()
+
+        self.attn_norm = AdaLayerNormZero(dim)
+        self.attn = Attention(
+            processor=AttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+        )
+
+        self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, t, mask=None, rope=None):  # x: noised input, t: time embedding
+        # pre-norm & modulation for attention input
+        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)
+
+        # attention
+        attn_output = self.attn(x=norm, mask=mask, rope=rope)
+
+        # process attention output for input x
+        x = x + gate_msa.unsqueeze(1) * attn_output
+
+        ff_norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        ff_output = self.ff(ff_norm)
+        x = x + gate_mlp.unsqueeze(1) * ff_output
+
+        return x
+
+
+# MMDiT Block https://arxiv.org/abs/2403.03206
+
+
+class MMDiTBlock(nn.Module):
+    r"""
+    modified from diffusers/src/diffusers/models/attention.py
+
+    notes.
+    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
+    _x: noised input related. (right part)
+    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
+    """
+
+    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False):
+        super().__init__()
+
+        self.context_pre_only = context_pre_only
+
+        self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim)
+        self.attn_norm_x = AdaLayerNormZero(dim)
+        self.attn = Attention(
+            processor=JointAttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+            context_dim=dim,
+            context_pre_only=context_pre_only,
+        )
+
+        if not context_pre_only:
+            self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+            self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+        else:
+            self.ff_norm_c = None
+            self.ff_c = None
+        self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, c, t, mask=None, rope=None, c_rope=None):  # x: noised input, c: context, t: time embedding
+        # pre-norm & modulation for attention input
+        if self.context_pre_only:
+            norm_c = self.attn_norm_c(c, t)
+        else:
+            norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t)
+        norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t)
+
+        # attention
+        x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope)
+
+        # process attention output for context c
+        if self.context_pre_only:
+            c = None
+        else:  # if not last layer
+            c = c + c_gate_msa.unsqueeze(1) * c_attn_output
+
+            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+            c_ff_output = self.ff_c(norm_c)
+            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output
+
+        # process attention output for input x
+        x = x + x_gate_msa.unsqueeze(1) * x_attn_output
+
+        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
+        x_ff_output = self.ff_x(norm_x)
+        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output
+
+        return c, x
+
+
+# time step conditioning embedding
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, dim, freq_embed_dim=256):
+        super().__init__()
+        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
+        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+
+    def forward(self, timestep: float["b"]):  # noqa: F821
+        time_hidden = self.time_embed(timestep)
+        time_hidden = time_hidden.to(timestep.dtype)
+        time = self.time_mlp(time_hidden)  # b d
+        return time

third_party/CosyVoice/cosyvoice/flow/decoder.py

@@ -0,0 +1,494 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# 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 typing import Tuple
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import pack, rearrange, repeat
+from cosyvoice.utils.common import mask_to_bias
+from cosyvoice.utils.mask import add_optional_chunk_mask
+from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D
+from matcha.models.components.transformer import BasicTransformerBlock
+
+
+class Transpose(torch.nn.Module):
+    def __init__(self, dim0: int, dim1: int):
+        super().__init__()
+        self.dim0 = dim0
+        self.dim1 = dim1
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = torch.transpose(x, self.dim0, self.dim1)
+        return x
+
+
+class CausalConv1d(torch.nn.Conv1d):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        dilation: int = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = 'zeros',
+        device=None,
+        dtype=None
+    ) -> None:
+        super(CausalConv1d, self).__init__(in_channels, out_channels,
+                                           kernel_size, stride,
+                                           padding=0, dilation=dilation,
+                                           groups=groups, bias=bias,
+                                           padding_mode=padding_mode,
+                                           device=device, dtype=dtype)
+        assert stride == 1
+        self.causal_padding = kernel_size - 1
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.pad(x, (self.causal_padding, 0), value=0.0)
+        x = super(CausalConv1d, self).forward(x)
+        return x
+
+
+class CausalBlock1D(Block1D):
+    def __init__(self, dim: int, dim_out: int):
+        super(CausalBlock1D, self).__init__(dim, dim_out)
+        self.block = torch.nn.Sequential(
+            CausalConv1d(dim, dim_out, 3),
+            Transpose(1, 2),
+            nn.LayerNorm(dim_out),
+            Transpose(1, 2),
+            nn.Mish(),
+        )
+
+    def forward(self, x: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        output = self.block(x * mask)
+        return output * mask
+
+
+class CausalResnetBlock1D(ResnetBlock1D):
+    def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
+        super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
+        self.block1 = CausalBlock1D(dim, dim_out)
+        self.block2 = CausalBlock1D(dim_out, dim_out)
+
+
+class ConditionalDecoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        channels=(256, 256),
+        dropout=0.05,
+        attention_head_dim=64,
+        n_blocks=1,
+        num_mid_blocks=2,
+        num_heads=4,
+        act_fn="snake",
+    ):
+        """
+        This decoder requires an input with the same shape of the target. So, if your text content
+        is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
+        """
+        super().__init__()
+        channels = tuple(channels)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.time_embeddings = SinusoidalPosEmb(in_channels)
+        time_embed_dim = channels[0] * 4
+        self.time_mlp = TimestepEmbedding(
+            in_channels=in_channels,
+            time_embed_dim=time_embed_dim,
+            act_fn="silu",
+        )
+        self.down_blocks = nn.ModuleList([])
+        self.mid_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        output_channel = in_channels
+        for i in range(len(channels)):  # pylint: disable=consider-using-enumerate
+            input_channel = output_channel
+            output_channel = channels[i]
+            is_last = i == len(channels) - 1
+            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            downsample = (
+                Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+            )
+            self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
+
+        for _ in range(num_mid_blocks):
+            input_channel = channels[-1]
+            out_channels = channels[-1]
+            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+
+            self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
+
+        channels = channels[::-1] + (channels[0],)
+        for i in range(len(channels) - 1):
+            input_channel = channels[i] * 2
+            output_channel = channels[i + 1]
+            is_last = i == len(channels) - 2
+            resnet = ResnetBlock1D(
+                dim=input_channel,
+                dim_out=output_channel,
+                time_emb_dim=time_embed_dim,
+            )
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            upsample = (
+                Upsample1D(output_channel, use_conv_transpose=True)
+                if not is_last
+                else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+            )
+            self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
+        self.final_block = Block1D(channels[-1], channels[-1])
+        self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.GroupNorm):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
+        """Forward pass of the UNet1DConditional model.
+
+        Args:
+            x (torch.Tensor): shape (batch_size, in_channels, time)
+            mask (_type_): shape (batch_size, 1, time)
+            t (_type_): shape (batch_size)
+            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+            cond (_type_, optional): placeholder for future use. Defaults to None.
+
+        Raises:
+            ValueError: _description_
+            ValueError: _description_
+
+        Returns:
+            _type_: _description_
+        """
+
+        t = self.time_embeddings(t).to(t.dtype)
+        t = self.time_mlp(t)
+
+        x = pack([x, mu], "b * t")[0]
+
+        if spks is not None:
+            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
+            x = pack([x, spks], "b * t")[0]
+        if cond is not None:
+            x = pack([x, cond], "b * t")[0]
+
+        hiddens = []
+        masks = [mask]
+        for resnet, transformer_blocks, downsample in self.down_blocks:
+            mask_down = masks[-1]
+            x = resnet(x, mask_down, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            hiddens.append(x)  # Save hidden states for skip connections
+            x = downsample(x * mask_down)
+            masks.append(mask_down[:, :, ::2])
+        masks = masks[:-1]
+        mask_mid = masks[-1]
+
+        for resnet, transformer_blocks in self.mid_blocks:
+            x = resnet(x, mask_mid, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+
+        for resnet, transformer_blocks, upsample in self.up_blocks:
+            mask_up = masks.pop()
+            skip = hiddens.pop()
+            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
+            x = resnet(x, mask_up, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            x = upsample(x * mask_up)
+        x = self.final_block(x, mask_up)
+        output = self.final_proj(x * mask_up)
+        return output * mask
+
+
+class CausalConditionalDecoder(ConditionalDecoder):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        channels=(256, 256),
+        dropout=0.05,
+        attention_head_dim=64,
+        n_blocks=1,
+        num_mid_blocks=2,
+        num_heads=4,
+        act_fn="snake",
+        static_chunk_size=50,
+        num_decoding_left_chunks=2,
+    ):
+        """
+        This decoder requires an input with the same shape of the target. So, if your text content
+        is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
+        """
+        torch.nn.Module.__init__(self)
+        channels = tuple(channels)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.time_embeddings = SinusoidalPosEmb(in_channels)
+        time_embed_dim = channels[0] * 4
+        self.time_mlp = TimestepEmbedding(
+            in_channels=in_channels,
+            time_embed_dim=time_embed_dim,
+            act_fn="silu",
+        )
+        self.static_chunk_size = static_chunk_size
+        self.num_decoding_left_chunks = num_decoding_left_chunks
+        self.down_blocks = nn.ModuleList([])
+        self.mid_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        output_channel = in_channels
+        for i in range(len(channels)):  # pylint: disable=consider-using-enumerate
+            input_channel = output_channel
+            output_channel = channels[i]
+            is_last = i == len(channels) - 1
+            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            downsample = (
+                Downsample1D(output_channel) if not is_last else CausalConv1d(output_channel, output_channel, 3)
+            )
+            self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
+
+        for _ in range(num_mid_blocks):
+            input_channel = channels[-1]
+            out_channels = channels[-1]
+            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+
+            self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
+
+        channels = channels[::-1] + (channels[0],)
+        for i in range(len(channels) - 1):
+            input_channel = channels[i] * 2
+            output_channel = channels[i + 1]
+            is_last = i == len(channels) - 2
+            resnet = CausalResnetBlock1D(
+                dim=input_channel,
+                dim_out=output_channel,
+                time_emb_dim=time_embed_dim,
+            )
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            upsample = (
+                Upsample1D(output_channel, use_conv_transpose=True)
+                if not is_last
+                else CausalConv1d(output_channel, output_channel, 3)
+            )
+            self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
+        self.final_block = CausalBlock1D(channels[-1], channels[-1])
+        self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
+        self.initialize_weights()
+
+    def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
+        """Forward pass of the UNet1DConditional model.
+
+        Args:
+            x (torch.Tensor): shape (batch_size, in_channels, time)
+            mask (_type_): shape (batch_size, 1, time)
+            t (_type_): shape (batch_size)
+            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+            cond (_type_, optional): placeholder for future use. Defaults to None.
+
+        Raises:
+            ValueError: _description_
+            ValueError: _description_
+
+        Returns:
+            _type_: _description_
+        """
+        t = self.time_embeddings(t).to(t.dtype)
+        t = self.time_mlp(t)
+
+        x = pack([x, mu], "b * t")[0]
+
+        if spks is not None:
+            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
+            x = pack([x, spks], "b * t")[0]
+        if cond is not None:
+            x = pack([x, cond], "b * t")[0]
+
+        hiddens = []
+        masks = [mask]
+        for resnet, transformer_blocks, downsample in self.down_blocks:
+            mask_down = masks[-1]
+            x = resnet(x, mask_down, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            if streaming is True:
+                attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, -1)
+            else:
+                attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            hiddens.append(x)  # Save hidden states for skip connections
+            x = downsample(x * mask_down)
+            masks.append(mask_down[:, :, ::2])
+        masks = masks[:-1]
+        mask_mid = masks[-1]
+
+        for resnet, transformer_blocks in self.mid_blocks:
+            x = resnet(x, mask_mid, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            if streaming is True:
+                attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, -1)
+            else:
+                attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+
+        for resnet, transformer_blocks, upsample in self.up_blocks:
+            mask_up = masks.pop()
+            skip = hiddens.pop()
+            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
+            x = resnet(x, mask_up, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            if streaming is True:
+                attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, -1)
+            else:
+                attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
+            attn_mask = mask_to_bias(attn_mask, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            x = upsample(x * mask_up)
+        x = self.final_block(x, mask_up)
+        output = self.final_proj(x * mask_up)
+        return output * mask

third_party/CosyVoice/cosyvoice/flow/flow.py

@@ -0,0 +1,443 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# 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, logging
+import random
+from typing import Dict, Optional
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from omegaconf import DictConfig
+from cosyvoice.utils.mask import make_pad_mask
+from cosyvoice.utils.onnx import SpeechTokenExtractor, online_feature, onnx_path
+
+
+class MaskedDiffWithXvec(torch.nn.Module):
+    def __init__(self,
+                 input_size: int = 512,
+                 output_size: int = 80,
+                 spk_embed_dim: int = 192,
+                 output_type: str = "mel",
+                 vocab_size: int = 4096,
+                 input_frame_rate: int = 50,
+                 only_mask_loss: bool = True,
+                 encoder: torch.nn.Module = None,
+                 length_regulator: torch.nn.Module = None,
+                 decoder: torch.nn.Module = None,
+                 decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
+                                       'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
+                                                                 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
+                                       'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
+                                                          'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+        self.decoder_conf = decoder_conf
+        self.vocab_size = vocab_size
+        self.output_type = output_type
+        self.input_frame_rate = input_frame_rate
+        logging.info(f"input frame rate={self.input_frame_rate}")
+        self.input_embedding = nn.Embedding(vocab_size, input_size)
+        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
+        self.encoder = encoder
+        self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
+        self.decoder = decoder
+        self.length_regulator = length_regulator
+        self.only_mask_loss = only_mask_loss
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        token = batch['speech_token'].to(device)
+        token_len = batch['speech_token_len'].to(device)
+        feat = batch['speech_feat'].to(device)
+        feat_len = batch['speech_feat_len'].to(device)
+        embedding = batch['embedding'].to(device)
+
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        h, h_lengths = self.encoder(token, token_len)
+        h = self.encoder_proj(h)
+        h, h_lengths = self.length_regulator(h, feat_len)
+
+        # get conditions
+        conds = torch.zeros(feat.shape, device=token.device)
+        for i, j in enumerate(feat_len):
+            if random.random() < 0.5:
+                continue
+            index = random.randint(0, int(0.3 * j))
+            conds[i, :index] = feat[i, :index]
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(feat_len)).to(h)
+        # NOTE this is unnecessary, feat/h already same shape
+        loss, _ = self.decoder.compute_loss(
+            feat.transpose(1, 2).contiguous(),
+            mask.unsqueeze(1),
+            h.transpose(1, 2).contiguous(),
+            embedding,
+            cond=conds
+        )
+        return {'loss': loss}
+
+    @torch.inference_mode()
+    def inference(self,
+                  token,
+                  token_len,
+                  prompt_token,
+                  prompt_token_len,
+                  prompt_feat,
+                  prompt_feat_len,
+                  embedding,
+                  flow_cache):
+        assert token.shape[0] == 1
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat speech token and prompt speech token
+        token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
+        token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
+        mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        h, h_lengths = self.encoder(token, token_len)
+        h = self.encoder_proj(h)
+        mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256)
+        h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate)
+
+        # get conditions
+        conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
+        conds[:, :mel_len1] = prompt_feat
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
+        feat, flow_cache = self.decoder(
+            mu=h.transpose(1, 2).contiguous(),
+            mask=mask.unsqueeze(1),
+            spks=embedding,
+            cond=conds,
+            n_timesteps=10,
+            prompt_len=mel_len1,
+            cache=flow_cache
+        )
+        feat = feat[:, :, mel_len1:]
+        assert feat.shape[2] == mel_len2
+        return feat.float(), flow_cache
+
+
+class CausalMaskedDiffWithXvec(torch.nn.Module):
+    def __init__(self,
+                 input_size: int = 512,
+                 output_size: int = 80,
+                 spk_embed_dim: int = 192,
+                 output_type: str = "mel",
+                 vocab_size: int = 4096,
+                 input_frame_rate: int = 50,
+                 only_mask_loss: bool = True,
+                 token_mel_ratio: int = 2,
+                 pre_lookahead_len: int = 3,
+                 encoder: torch.nn.Module = None,
+                 decoder: torch.nn.Module = None,
+                 decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
+                                       'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
+                                                                 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
+                                       'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
+                                                          'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+        self.decoder_conf = decoder_conf
+        self.vocab_size = vocab_size
+        self.output_type = output_type
+        self.input_frame_rate = input_frame_rate
+        logging.info(f"input frame rate={self.input_frame_rate}")
+        self.input_embedding = nn.Embedding(vocab_size, input_size)
+        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
+        self.encoder = encoder
+        self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
+        self.decoder = decoder
+        self.only_mask_loss = only_mask_loss
+        self.token_mel_ratio = token_mel_ratio
+        self.pre_lookahead_len = pre_lookahead_len
+        if online_feature is True:
+            self.speech_token_extractor = SpeechTokenExtractor(model_path=os.path.join(onnx_path, 'speech_tokenizer_v2.batch.onnx'))
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        if 'speech_token' not in batch:
+            token, token_len = self.speech_token_extractor.inference(batch['whisper_feat'], batch['whisper_feat_len'], device)
+        else:
+            token = batch['speech_token'].to(device)
+            token_len = batch['speech_token_len'].to(device)
+        feat = batch['speech_feat'].to(device)
+        feat_len = batch['speech_feat_len'].to(device)
+        embedding = batch['embedding'].to(device)
+
+        # NOTE unified training, static_chunk_size > 0 or = 0
+        streaming = True if random.random() < 0.5 else False
+
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        h, h_lengths = self.encoder(token, token_len, streaming=streaming)
+        h = self.encoder_proj(h)
+
+        # get conditions
+        conds = torch.zeros(feat.shape, device=token.device)
+        for i, j in enumerate(feat_len):
+            if random.random() < 0.5:
+                continue
+            index = random.randint(0, int(0.3 * j))
+            conds[i, :index] = feat[i, :index]
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h)
+        loss, _ = self.decoder.compute_loss(
+            feat.transpose(1, 2).contiguous(),
+            mask.unsqueeze(1),
+            h.transpose(1, 2).contiguous(),
+            embedding,
+            cond=conds,
+            streaming=streaming,
+        )
+        return {'loss': loss}
+
+    @torch.inference_mode()
+    def inference(self,
+                  token,
+                  token_len,
+                  prompt_token,
+                  prompt_token_len,
+                  prompt_feat,
+                  prompt_feat_len,
+                  embedding,
+                  streaming,
+                  finalize):
+        assert token.shape[0] == 1
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
+        mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        if finalize is True:
+            h, h_lengths = self.encoder(token, token_len, streaming=streaming)
+        else:
+            token, context = token[:, :-self.pre_lookahead_len], token[:, -self.pre_lookahead_len:]
+            h, h_lengths = self.encoder(token, token_len, context=context, streaming=streaming)
+        mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
+        h = self.encoder_proj(h)
+
+        # get conditions
+        conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
+        conds[:, :mel_len1] = prompt_feat
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
+        feat, _ = self.decoder(
+            mu=h.transpose(1, 2).contiguous(),
+            mask=mask.unsqueeze(1),
+            spks=embedding,
+            cond=conds,
+            n_timesteps=10,
+            streaming=streaming
+        )
+        feat = feat[:, :, mel_len1:]
+        assert feat.shape[2] == mel_len2
+        return feat.float(), None
+
+
+class CausalMaskedDiffWithDiT(torch.nn.Module):
+    def __init__(self,
+                 input_size: int = 512,
+                 output_size: int = 80,
+                 spk_embed_dim: int = 192,
+                 output_type: str = "mel",
+                 vocab_size: int = 4096,
+                 input_frame_rate: int = 50,
+                 only_mask_loss: bool = True,
+                 token_mel_ratio: int = 2,
+                 pre_lookahead_len: int = 3,
+                 pre_lookahead_layer: torch.nn.Module = None,
+                 decoder: torch.nn.Module = None,
+                 decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
+                                       'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
+                                                                 'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
+                                       'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
+                                                          'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+        self.decoder_conf = decoder_conf
+        self.vocab_size = vocab_size
+        self.output_type = output_type
+        self.input_frame_rate = input_frame_rate
+        logging.info(f"input frame rate={self.input_frame_rate}")
+        self.input_embedding = nn.Embedding(vocab_size, input_size)
+        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
+        self.pre_lookahead_len = pre_lookahead_len
+        self.pre_lookahead_layer = pre_lookahead_layer
+        self.decoder = decoder
+        self.only_mask_loss = only_mask_loss
+        self.token_mel_ratio = token_mel_ratio
+        if online_feature is True:
+            self.speech_token_extractor = SpeechTokenExtractor(model_path=os.path.join(onnx_path, 'speech_tokenizer_v3.batch.onnx'))
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        if 'speech_token' not in batch:
+            token, token_len = self.speech_token_extractor.inference(batch['whisper_feat'], batch['whisper_feat_len'], device)
+        else:
+            token = batch['speech_token'].to(device)
+            token_len = batch['speech_token_len'].to(device)
+        feat = batch['speech_feat'].to(device)
+        feat_len = batch['speech_feat_len'].to(device)
+        embedding = batch['embedding'].to(device)
+
+        # NOTE unified training, static_chunk_size > 0 or = 0
+        streaming = True if random.random() < 0.5 else False
+
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        h = self.pre_lookahead_layer(token)
+        h = h.repeat_interleave(self.token_mel_ratio, dim=1)
+        mask = mask.repeat_interleave(self.token_mel_ratio, dim=1).squeeze(dim=-1)
+
+        # get conditions
+        conds = torch.zeros(feat.shape, device=token.device)
+        for i, j in enumerate(feat_len):
+            if random.random() < 0.5:
+                continue
+            index = random.randint(0, int(0.3 * j))
+            conds[i, :index] = feat[i, :index]
+        conds = conds.transpose(1, 2)
+
+        loss, _ = self.decoder.compute_loss(
+            feat.transpose(1, 2).contiguous(),
+            mask.unsqueeze(1),
+            h.transpose(1, 2).contiguous(),
+            embedding,
+            cond=conds,
+            streaming=streaming,
+        )
+        return {'loss': loss}
+
+    @torch.inference_mode()
+    def inference(self,
+                  token,
+                  token_len,
+                  prompt_token,
+                  prompt_token_len,
+                  prompt_feat,
+                  prompt_feat_len,
+                  embedding,
+                  streaming,
+                  finalize):
+        assert token.shape[0] == 1
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
+        mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        if finalize is True:
+            h = self.pre_lookahead_layer(token)
+        else:
+            h = self.pre_lookahead_layer(token[:, :-self.pre_lookahead_len], context=token[:, -self.pre_lookahead_len:])
+        h = h.repeat_interleave(self.token_mel_ratio, dim=1)
+        mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
+
+        # get conditions
+        conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
+        conds[:, :mel_len1] = prompt_feat
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
+        feat, _ = self.decoder(
+            mu=h.transpose(1, 2).contiguous(),
+            mask=mask.unsqueeze(1),
+            spks=embedding,
+            cond=conds,
+            n_timesteps=10,
+            streaming=streaming
+        )
+        feat = feat[:, :, mel_len1:]
+        assert feat.shape[2] == mel_len2
+        return feat.float(), None
+
+
+if __name__ == '__main__':
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    from hyperpyyaml import load_hyperpyyaml
+    with open('./pretrained_models/Fun-CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f:
+        configs = load_hyperpyyaml(f, overrides={'llm': None, 'hift': None})
+    model = configs['flow']
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model.to(device)
+    model.eval()
+    max_len = 10 * model.decoder.estimator.static_chunk_size
+    chunk_size = model.decoder.estimator.static_chunk_size
+    context_size = model.pre_lookahead_layer.pre_lookahead_len
+    token = torch.randint(0, 6561, size=(1, max_len)).to(device)
+    token_len = torch.tensor([max_len]).to(device)
+    prompt_token = torch.randint(0, 6561, size=(1, chunk_size)).to(device)
+    prompt_token_len = torch.tensor([chunk_size]).to(device)
+    prompt_feat = torch.rand(1, chunk_size * 2, 80).to(device)
+    prompt_feat_len = torch.tensor([chunk_size * 2]).to(device)
+    prompt_embedding = torch.rand(1, 192).to(device)
+    pred_gt, _ = model.inference(token, token_len, prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=True)
+    for i in range(0, max_len, chunk_size):
+        finalize = True if i + chunk_size + context_size >= max_len else False
+        pred_chunk, _ = model.inference(token[:, :i + chunk_size + context_size], torch.tensor([token[:, :i + chunk_size + context_size].shape[1]]).to(device),
+                                        prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=finalize)
+        pred_chunk = pred_chunk[:, :, i * model.token_mel_ratio:]
+        print((pred_gt[:, :, i * model.token_mel_ratio: i * model.token_mel_ratio + pred_chunk.shape[2]] - pred_chunk).abs().max().item())

third_party/CosyVoice/cosyvoice/flow/flow_matching.py

@@ -0,0 +1,227 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#               2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
+#
+# 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 torch
+import torch.nn.functional as F
+from matcha.models.components.flow_matching import BASECFM
+from cosyvoice.utils.common import set_all_random_seed
+
+
+class ConditionalCFM(BASECFM):
+    def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
+        super().__init__(
+            n_feats=in_channels,
+            cfm_params=cfm_params,
+            n_spks=n_spks,
+            spk_emb_dim=spk_emb_dim,
+        )
+        self.t_scheduler = cfm_params.t_scheduler
+        self.training_cfg_rate = cfm_params.training_cfg_rate
+        self.inference_cfg_rate = cfm_params.inference_cfg_rate
+        in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
+        # Just change the architecture of the estimator here
+        self.estimator = estimator
+
+    @torch.inference_mode()
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, cache=torch.zeros(1, 80, 0, 2)):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+
+        z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature
+        cache_size = cache.shape[2]
+        # fix prompt and overlap part mu and z
+        if cache_size != 0:
+            z[:, :, :cache_size] = cache[:, :, :, 0]
+            mu[:, :, :cache_size] = cache[:, :, :, 1]
+        z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2)
+        mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2)
+        cache = torch.stack([z_cache, mu_cache], dim=-1)
+
+        t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
+        if self.t_scheduler == 'cosine':
+            t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
+        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), cache
+
+    def solve_euler(self, x, t_span, mu, mask, spks, cond, streaming=False):
+        """
+        Fixed euler solver for ODEs.
+        Args:
+            x (torch.Tensor): random noise
+            t_span (torch.Tensor): n_timesteps interpolated
+                shape: (n_timesteps + 1,)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+        """
+        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+        t = t.unsqueeze(dim=0)
+
+        # I am storing this because I can later plot it by putting a debugger here and saving it to a file
+        # Or in future might add like a return_all_steps flag
+        sol = []
+
+        # Do not use concat, it may cause memory format changed and trt infer with wrong results!
+        # NOTE when flow run in amp mode, x.dtype is float32, which cause nan in trt fp16 inference, so set dtype=spks.dtype
+        x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
+        mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=spks.dtype)
+        mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
+        t_in = torch.zeros([2], device=x.device, dtype=spks.dtype)
+        spks_in = torch.zeros([2, 80], device=x.device, dtype=spks.dtype)
+        cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
+        for step in range(1, len(t_span)):
+            # Classifier-Free Guidance inference introduced in VoiceBox
+            x_in[:] = x
+            mask_in[:] = mask
+            mu_in[0] = mu
+            t_in[:] = t.unsqueeze(0)
+            spks_in[0] = spks
+            cond_in[0] = cond
+            dphi_dt = self.forward_estimator(
+                x_in, mask_in,
+                mu_in, t_in,
+                spks_in,
+                cond_in,
+                streaming
+            )
+            dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0)
+            dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt)
+            x = x + dt * dphi_dt
+            t = t + dt
+            sol.append(x)
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
+
+        return sol[-1].float()
+
+    def forward_estimator(self, x, mask, mu, t, spks, cond, streaming=False):
+        if isinstance(self.estimator, torch.nn.Module):
+            return self.estimator(x, mask, mu, t, spks, cond, streaming=streaming)
+        else:
+            [estimator, stream], trt_engine = self.estimator.acquire_estimator()
+            # NOTE need to synchronize when switching stream
+            torch.cuda.current_stream().synchronize()
+            with stream:
+                estimator.set_input_shape('x', (2, 80, x.size(2)))
+                estimator.set_input_shape('mask', (2, 1, x.size(2)))
+                estimator.set_input_shape('mu', (2, 80, x.size(2)))
+                estimator.set_input_shape('t', (2,))
+                estimator.set_input_shape('spks', (2, 80))
+                estimator.set_input_shape('cond', (2, 80, x.size(2)))
+                data_ptrs = [x.contiguous().data_ptr(),
+                             mask.contiguous().data_ptr(),
+                             mu.contiguous().data_ptr(),
+                             t.contiguous().data_ptr(),
+                             spks.contiguous().data_ptr(),
+                             cond.contiguous().data_ptr(),
+                             x.data_ptr()]
+                for i, j in enumerate(data_ptrs):
+                    estimator.set_tensor_address(trt_engine.get_tensor_name(i), j)
+                # run trt engine
+                assert estimator.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True
+                torch.cuda.current_stream().synchronize()
+            self.estimator.release_estimator(estimator, stream)
+            return x
+
+    def compute_loss(self, x1, mask, mu, spks=None, cond=None, streaming=False):
+        """Computes diffusion loss
+
+        Args:
+            x1 (torch.Tensor): Target
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): target mask
+                shape: (batch_size, 1, mel_timesteps)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            spks (torch.Tensor, optional): speaker embedding. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+
+        Returns:
+            loss: conditional flow matching loss
+            y: conditional flow
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        b, _, t = mu.shape
+
+        # random timestep
+        t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
+
+        # sample noise p(x_0)
+        z = torch.randn_like(x1)
+
+        y = (1 - (1 - self.sigma_min) * t) * z + t * x1
+        u = x1 - (1 - self.sigma_min) * z
+
+        # during training, we randomly drop condition to trade off mode coverage and sample fidelity
+        if self.training_cfg_rate > 0:
+            cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate
+            mu = mu * cfg_mask.view(-1, 1, 1)
+            spks = spks * cfg_mask.view(-1, 1)
+            cond = cond * cfg_mask.view(-1, 1, 1)
+
+        pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond, streaming=streaming)
+        loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
+        return loss, y
+
+
+class CausalConditionalCFM(ConditionalCFM):
+    def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
+        super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator)
+        set_all_random_seed(0)
+        self.rand_noise = torch.randn([1, 80, 50 * 300])
+
+    @torch.inference_mode()
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, streaming=False):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+
+        z = self.rand_noise[:, :, :mu.size(2)].to(mu.device).to(mu.dtype) * temperature
+        # fix prompt and overlap part mu and z
+        t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
+        if self.t_scheduler == 'cosine':
+            t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
+        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond, streaming=streaming), None

third_party/CosyVoice/cosyvoice/flow/length_regulator.py

@@ -0,0 +1,70 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# 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 typing import Tuple
+import torch.nn as nn
+import torch
+from torch.nn import functional as F
+from cosyvoice.utils.mask import make_pad_mask
+
+
+class InterpolateRegulator(nn.Module):
+    def __init__(
+            self,
+            channels: int,
+            sampling_ratios: Tuple,
+            out_channels: int = None,
+            groups: int = 1,
+    ):
+        super().__init__()
+        self.sampling_ratios = sampling_ratios
+        out_channels = out_channels or channels
+        model = nn.ModuleList([])
+        if len(sampling_ratios) > 0:
+            for _ in sampling_ratios:
+                module = nn.Conv1d(channels, channels, 3, 1, 1)
+                norm = nn.GroupNorm(groups, channels)
+                act = nn.Mish()
+                model.extend([module, norm, act])
+        model.append(
+            nn.Conv1d(channels, out_channels, 1, 1)
+        )
+        self.model = nn.Sequential(*model)
+
+    def forward(self, x, ylens=None):
+        # x in (B, T, D)
+        mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1)
+        x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='linear')
+        out = self.model(x).transpose(1, 2).contiguous()
+        olens = ylens
+        return out * mask, olens
+
+    def inference(self, x1, x2, mel_len1, mel_len2, input_frame_rate=50):
+        # in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel
+        # NOTE 20 corresponds to token_overlap_len in cosyvoice/cli/model.py
+        # x in (B, T, D)
+        if x2.shape[1] > 40:
+            x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')
+            x2_mid = F.interpolate(x2[:, 20:-20].transpose(1, 2).contiguous(), size=mel_len2 - int(20 / input_frame_rate * 22050 / 256) * 2,
+                                   mode='linear')
+            x2_tail = F.interpolate(x2[:, -20:].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')
+            x2 = torch.concat([x2_head, x2_mid, x2_tail], dim=2)
+        else:
+            x2 = F.interpolate(x2.transpose(1, 2).contiguous(), size=mel_len2, mode='linear')
+        if x1.shape[1] != 0:
+            x1 = F.interpolate(x1.transpose(1, 2).contiguous(), size=mel_len1, mode='linear')
+            x = torch.concat([x1, x2], dim=2)
+        else:
+            x = x2
+        out = self.model(x).transpose(1, 2).contiguous()
+        return out, mel_len1 + mel_len2

third_party/CosyVoice/cosyvoice/hifigan/discriminator.py

@@ -0,0 +1,230 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+try:
+    from torch.nn.utils.parametrizations import weight_norm, spectral_norm
+except ImportError:
+    from torch.nn.utils import weight_norm, spectral_norm
+from typing import List, Optional, Tuple
+from einops import rearrange
+from torchaudio.transforms import Spectrogram
+
+LRELU_SLOPE = 0.1
+
+
+class MultipleDiscriminator(nn.Module):
+    def __init__(
+            self, mpd: nn.Module, mrd: nn.Module
+    ):
+        super().__init__()
+        self.mpd = mpd
+        self.mrd = mrd
+
+    def forward(self, y: torch.Tensor, y_hat: torch.Tensor):
+        y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], []
+        this_y_d_rs, this_y_d_gs, this_fmap_rs, this_fmap_gs = self.mpd(y.unsqueeze(dim=1), y_hat.unsqueeze(dim=1))
+        y_d_rs += this_y_d_rs
+        y_d_gs += this_y_d_gs
+        fmap_rs += this_fmap_rs
+        fmap_gs += this_fmap_gs
+        this_y_d_rs, this_y_d_gs, this_fmap_rs, this_fmap_gs = self.mrd(y, y_hat)
+        y_d_rs += this_y_d_rs
+        y_d_gs += this_y_d_gs
+        fmap_rs += this_fmap_rs
+        fmap_gs += this_fmap_gs
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class MultiResolutionDiscriminator(nn.Module):
+    def __init__(
+        self,
+        fft_sizes: Tuple[int, ...] = (2048, 1024, 512),
+        num_embeddings: Optional[int] = None,
+    ):
+        """
+        Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec.
+        Additionally, it allows incorporating conditional information with a learned embeddings table.
+
+        Args:
+            fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512).
+            num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
+                Defaults to None.
+        """
+
+        super().__init__()
+        self.discriminators = nn.ModuleList(
+            [DiscriminatorR(window_length=w, num_embeddings=num_embeddings) for w in fft_sizes]
+        )
+
+    def forward(
+        self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None
+    ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+
+        for d in self.discriminators:
+            y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
+            y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorR(nn.Module):
+    def __init__(
+        self,
+        window_length: int,
+        num_embeddings: Optional[int] = None,
+        channels: int = 32,
+        hop_factor: float = 0.25,
+        bands: Tuple[Tuple[float, float], ...] = ((0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)),
+    ):
+        super().__init__()
+        self.window_length = window_length
+        self.hop_factor = hop_factor
+        self.spec_fn = Spectrogram(
+            n_fft=window_length, hop_length=int(window_length * hop_factor), win_length=window_length, power=None
+        )
+        n_fft = window_length // 2 + 1
+        bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
+        self.bands = bands
+        convs = lambda: nn.ModuleList(
+            [
+                weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
+                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+                weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))),
+            ]
+        )
+        self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
+
+        if num_embeddings is not None:
+            self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=channels)
+            torch.nn.init.zeros_(self.emb.weight)
+
+        self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1)))
+
+    def spectrogram(self, x):
+        # Remove DC offset
+        x = x - x.mean(dim=-1, keepdims=True)
+        # Peak normalize the volume of input audio
+        x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
+        x = self.spec_fn(x)
+        x = torch.view_as_real(x)
+        x = rearrange(x, "b f t c -> b c t f")
+        # Split into bands
+        x_bands = [x[..., b[0]: b[1]] for b in self.bands]
+        return x_bands
+
+    def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None):
+        x_bands = self.spectrogram(x)
+        fmap = []
+        x = []
+        for band, stack in zip(x_bands, self.band_convs):
+            for i, layer in enumerate(stack):
+                band = layer(band)
+                band = torch.nn.functional.leaky_relu(band, 0.1)
+                if i > 0:
+                    fmap.append(band)
+            x.append(band)
+        x = torch.cat(x, dim=-1)
+        if cond_embedding_id is not None:
+            emb = self.emb(cond_embedding_id)
+            h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
+        else:
+            h = 0
+        x = self.conv_post(x)
+        fmap.append(x)
+        x += h
+
+        return x, fmap
+
+
+class MultiResSpecDiscriminator(torch.nn.Module):
+
+    def __init__(self,
+                 fft_sizes=[1024, 2048, 512],
+                 hop_sizes=[120, 240, 50],
+                 win_lengths=[600, 1200, 240],
+                 window="hann_window"):
+
+        super(MultiResSpecDiscriminator, self).__init__()
+        self.discriminators = nn.ModuleList([
+            SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window),
+            SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window),
+            SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)])
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for _, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+def stft(x, fft_size, hop_size, win_length, window):
+    """Perform STFT and convert to magnitude spectrogram.
+    Args:
+        x (Tensor): Input signal tensor (B, T).
+        fft_size (int): FFT size.
+        hop_size (int): Hop size.
+        win_length (int): Window length.
+        window (str): Window function type.
+    Returns:
+        Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
+    """
+    x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True)
+
+    # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
+    return torch.abs(x_stft).transpose(2, 1)
+
+
+class SpecDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False):
+        super(SpecDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.fft_size = fft_size
+        self.shift_size = shift_size
+        self.win_length = win_length
+        self.window = getattr(torch, window)(win_length)
+        self.discriminators = nn.ModuleList([
+            norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))),
+            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
+            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
+            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))),
+            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))),
+        ])
+
+        self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))
+
+    def forward(self, y):
+
+        fmap = []
+        y = y.squeeze(1)
+        y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.device))
+        y = y.unsqueeze(1)
+        for _, d in enumerate(self.discriminators):
+            y = d(y)
+            y = F.leaky_relu(y, LRELU_SLOPE)
+            fmap.append(y)
+
+        y = self.out(y)
+        fmap.append(y)
+
+        return torch.flatten(y, 1, -1), fmap

third_party/CosyVoice/cosyvoice/hifigan/f0_predictor.py

@@ -0,0 +1,103 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
+#
+# 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 torch
+import torch.nn as nn
+try:
+    from torch.nn.utils.parametrizations import weight_norm
+except ImportError:
+    from torch.nn.utils import weight_norm
+from cosyvoice.transformer.convolution import CausalConv1d
+
+
+class ConvRNNF0Predictor(nn.Module):
+    def __init__(self,
+                 num_class: int = 1,
+                 in_channels: int = 80,
+                 cond_channels: int = 512
+                 ):
+        super().__init__()
+
+        self.num_class = num_class
+        self.condnet = nn.Sequential(
+            weight_norm(
+                nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+            weight_norm(
+                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+            weight_norm(
+                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+            weight_norm(
+                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+            weight_norm(
+                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+        )
+        self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.condnet(x)
+        x = x.transpose(1, 2)
+        return torch.abs(self.classifier(x).squeeze(-1))
+
+
+class CausalConvRNNF0Predictor(nn.Module):
+    def __init__(self,
+                 num_class: int = 1,
+                 in_channels: int = 80,
+                 cond_channels: int = 512
+                 ):
+        super().__init__()
+
+        self.num_class = num_class
+        self.condnet = nn.Sequential(
+            weight_norm(
+                CausalConv1d(in_channels, cond_channels, kernel_size=4, causal_type='right')
+            ),
+            nn.ELU(),
+            weight_norm(
+                CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left')
+            ),
+            nn.ELU(),
+            weight_norm(
+                CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left')
+            ),
+            nn.ELU(),
+            weight_norm(
+                CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left')
+            ),
+            nn.ELU(),
+            weight_norm(
+                CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left')
+            ),
+            nn.ELU(),
+        )
+        self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)
+
+    def forward(self, x: torch.Tensor, finalize: bool = True) -> torch.Tensor:
+        if finalize is True:
+            x = self.condnet[0](x)
+        else:
+            x = self.condnet[0](x[:, :, :-self.condnet[0].causal_padding], x[:, :, -self.condnet[0].causal_padding:])
+        for i in range(1, len(self.condnet)):
+            x = self.condnet[i](x)
+        x = x.transpose(1, 2)
+        return torch.abs(self.classifier(x).squeeze(-1))

third_party/CosyVoice/cosyvoice/hifigan/generator.py

@@ -0,0 +1,746 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
+#
+# 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.
+
+"""HIFI-GAN"""
+
+from typing import Dict, Optional, List
+import numpy as np
+from scipy.signal import get_window
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import Conv1d
+from torch.nn import ConvTranspose1d
+from torch.nn.utils import remove_weight_norm
+try:
+    from torch.nn.utils.parametrizations import weight_norm
+except ImportError:
+    from torch.nn.utils import weight_norm
+from torch.distributions.uniform import Uniform
+from cosyvoice.transformer.convolution import CausalConv1d, CausalConv1dDownSample, CausalConv1dUpsample
+from cosyvoice.transformer.activation import Snake
+from cosyvoice.utils.common import get_padding
+from cosyvoice.utils.common import init_weights
+
+
+"""hifigan based generator implementation.
+
+This code is modified from https://github.com/jik876/hifi-gan
+ ,https://github.com/kan-bayashi/ParallelWaveGAN and
+ https://github.com/NVIDIA/BigVGAN
+
+"""
+
+
+class ResBlock(torch.nn.Module):
+    """Residual block module in HiFiGAN/BigVGAN."""
+    def __init__(
+        self,
+        channels: int = 512,
+        kernel_size: int = 3,
+        dilations: List[int] = [1, 3, 5],
+        causal: bool = False,
+    ):
+        super(ResBlock, self).__init__()
+        self.causal = causal
+        self.convs1 = nn.ModuleList()
+        self.convs2 = nn.ModuleList()
+
+        for dilation in dilations:
+            self.convs1.append(
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation,
+                        padding=get_padding(kernel_size, dilation)) if causal is False else
+                    CausalConv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation,
+                        causal_type='left'
+                    )
+                )
+            )
+            self.convs2.append(
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1)) if causal is False else
+                    CausalConv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        causal_type='left'
+                    )
+                )
+            )
+        self.convs1.apply(init_weights)
+        self.convs2.apply(init_weights)
+        self.activations1 = nn.ModuleList([
+            Snake(channels, alpha_logscale=False)
+            for _ in range(len(self.convs1))
+        ])
+        self.activations2 = nn.ModuleList([
+            Snake(channels, alpha_logscale=False)
+            for _ in range(len(self.convs2))
+        ])
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        for idx in range(len(self.convs1)):
+            xt = self.activations1[idx](x)
+            xt = self.convs1[idx](xt)
+            xt = self.activations2[idx](xt)
+            xt = self.convs2[idx](xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for idx in range(len(self.convs1)):
+            remove_weight_norm(self.convs1[idx])
+            remove_weight_norm(self.convs2[idx])
+
+
+class SineGen(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, harmonic_num=0,
+                 sine_amp=0.1, noise_std=0.003,
+                 voiced_threshold=0):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = (f0 > self.voiced_threshold).type(torch.float32)
+        return uv
+
+    @torch.no_grad()
+    def forward(self, f0):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, dim=1, length)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        f0 = f0.transpose(1, 2)
+        F_mat = torch.zeros((f0.size(0), self.harmonic_num + 1, f0.size(-1))).to(f0.device)
+        for i in range(self.harmonic_num + 1):
+            F_mat[:, i: i + 1, :] = f0 * (i + 1) / self.sampling_rate
+
+        theta_mat = 2 * np.pi * (torch.cumsum(F_mat, dim=-1) % 1)
+        u_dist = Uniform(low=-np.pi, high=np.pi)
+        phase_vec = u_dist.sample(sample_shape=(f0.size(0), self.harmonic_num + 1, 1)).to(F_mat.device)
+        phase_vec[:, 0, :] = 0
+
+        # generate sine waveforms
+        sine_waves = self.sine_amp * torch.sin(theta_mat + phase_vec)
+
+        # generate uv signal
+        uv = self._f02uv(f0)
+
+        # noise: for unvoiced should be similar to sine_amp
+        #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+        # .       for voiced regions is self.noise_std
+        noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+        noise = noise_amp * torch.randn_like(sine_waves)
+
+        # first: set the unvoiced part to 0 by uv
+        # then: additive noise
+        sine_waves = sine_waves * uv + noise
+        return sine_waves.transpose(1, 2), uv.transpose(1, 2), noise
+
+
+class SineGen2(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, upsample_scale, harmonic_num=0,
+                 sine_amp=0.1, noise_std=0.003,
+                 voiced_threshold=0,
+                 flag_for_pulse=False,
+                 causal=False):
+        super(SineGen2, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.flag_for_pulse = flag_for_pulse
+        self.upsample_scale = upsample_scale
+        self.causal = causal
+        if causal is True:
+            self.rand_ini = torch.rand(1, 9)
+            self.rand_ini[:, 0] = 0
+            self.sine_waves = torch.rand(1, 300 * 24000, 9)
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = (f0 > self.voiced_threshold).type(torch.float32)
+        return uv
+
+    def _f02sine(self, f0_values):
+        """ f0_values: (batchsize, length, dim)
+            where dim indicates fundamental tone and overtones
+        """
+        # convert to F0 in rad. The interger part n can be ignored
+        # because 2 * np.pi * n doesn't affect phase
+        rad_values = (f0_values / self.sampling_rate) % 1
+
+        # initial phase noise (no noise for fundamental component)
+        if self.training is False and self.causal is True:
+            rad_values[:, 0, :] = rad_values[:, 0, :] + self.rand_ini.to(rad_values.device)
+        else:
+            rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], device=f0_values.device)
+            rand_ini[:, 0] = 0
+            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+        if not self.flag_for_pulse:
+            rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2),
+                                                         scale_factor=1 / self.upsample_scale,
+                                                         mode="linear").transpose(1, 2)
+
+            phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
+            phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale,
+                                                    scale_factor=self.upsample_scale, mode="nearest" if self.causal is True else 'linear').transpose(1, 2)
+            sines = torch.sin(phase)
+        else:
+            # If necessary, make sure that the first time step of every
+            # voiced segments is sin(pi) or cos(0)
+            # This is used for pulse-train generation
+
+            # identify the last time step in unvoiced segments
+            uv = self._f02uv(f0_values)
+            uv_1 = torch.roll(uv, shifts=-1, dims=1)
+            uv_1[:, -1, :] = 1
+            u_loc = (uv < 1) * (uv_1 > 0)
+
+            # get the instantanouse phase
+            tmp_cumsum = torch.cumsum(rad_values, dim=1)
+            # different batch needs to be processed differently
+            for idx in range(f0_values.shape[0]):
+                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+                # stores the accumulation of i.phase within
+                # each voiced segments
+                tmp_cumsum[idx, :, :] = 0
+                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+
+            # rad_values - tmp_cumsum: remove the accumulation of i.phase
+            # within the previous voiced segment.
+            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
+
+            # get the sines
+            sines = torch.cos(i_phase * 2 * np.pi)
+        return sines
+
+    def forward(self, f0):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        # fundamental component
+        fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
+
+        # generate sine waveforms
+        sine_waves = self._f02sine(fn) * self.sine_amp
+
+        # generate uv signal
+        uv = self._f02uv(f0)
+
+        # noise: for unvoiced should be similar to sine_amp
+        #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+        # .       for voiced regions is self.noise_std
+        noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+        if self.training is False and self.causal is True:
+            noise = noise_amp * self.sine_waves[:, :sine_waves.shape[1]].to(sine_waves.device)
+        else:
+            noise = noise_amp * torch.randn_like(sine_waves)
+
+        # first: set the unvoiced part to 0 by uv
+        # then: additive noise
+        sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """ SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0, sinegen_type='1', causal=False):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+
+        # to produce sine waveforms
+        if sinegen_type == '1':
+            self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod)
+        else:
+            self.l_sin_gen = SineGen2(sampling_rate, upsample_scale, harmonic_num, sine_amp, add_noise_std, voiced_threshod, causal=causal)
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+        self.causal = causal
+        if causal is True:
+            self.uv = torch.rand(1, 300 * 24000, 1)
+
+    def forward(self, x):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        # source for harmonic branch
+        with torch.no_grad():
+            sine_wavs, uv, _ = self.l_sin_gen(x)
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+        # source for noise branch, in the same shape as uv
+        if self.training is False and self.causal is True:
+            noise = self.uv[:, :uv.shape[1]] * self.sine_amp / 3
+        else:
+            noise = torch.randn_like(uv) * self.sine_amp / 3
+        return sine_merge, noise, uv
+
+
+class HiFTGenerator(nn.Module):
+    """
+    HiFTNet Generator: Neural Source Filter + ISTFTNet
+    https://arxiv.org/abs/2309.09493
+    """
+    def __init__(
+            self,
+            in_channels: int = 80,
+            base_channels: int = 512,
+            nb_harmonics: int = 8,
+            sampling_rate: int = 22050,
+            nsf_alpha: float = 0.1,
+            nsf_sigma: float = 0.003,
+            nsf_voiced_threshold: float = 10,
+            upsample_rates: List[int] = [8, 8],
+            upsample_kernel_sizes: List[int] = [16, 16],
+            istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4},
+            resblock_kernel_sizes: List[int] = [3, 7, 11],
+            resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+            source_resblock_kernel_sizes: List[int] = [7, 11],
+            source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]],
+            lrelu_slope: float = 0.1,
+            audio_limit: float = 0.99,
+            f0_predictor: torch.nn.Module = None,
+    ):
+        super(HiFTGenerator, self).__init__()
+
+        self.out_channels = 1
+        self.nb_harmonics = nb_harmonics
+        self.sampling_rate = sampling_rate
+        self.istft_params = istft_params
+        self.lrelu_slope = lrelu_slope
+        self.audio_limit = audio_limit
+
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        # NOTE in CosyVoice2, we use the original SineGen implementation
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=sampling_rate,
+            upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"],
+            harmonic_num=nb_harmonics,
+            sine_amp=nsf_alpha,
+            add_noise_std=nsf_sigma,
+            voiced_threshod=nsf_voiced_threshold,
+            sinegen_type='1' if self.sampling_rate == 22050 else '2',
+            causal=False)
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"])
+
+        self.conv_pre = weight_norm(
+            Conv1d(in_channels, base_channels, 7, 1, padding=3)
+        )
+
+        # Up
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        base_channels // (2**i),
+                        base_channels // (2**(i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        # Down
+        self.source_downs = nn.ModuleList()
+        self.source_resblocks = nn.ModuleList()
+        downsample_rates = [1] + upsample_rates[::-1][:-1]
+        downsample_cum_rates = np.cumprod(downsample_rates)
+        for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)):
+            if u == 1:
+                self.source_downs.append(
+                    Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1)
+                )
+            else:
+                self.source_downs.append(
+                    Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u, padding=(u // 2))
+                )
+
+            self.source_resblocks.append(
+                ResBlock(base_channels // (2 ** (i + 1)), k, d)
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = base_channels // (2**(i + 1))
+            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(ResBlock(ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, istft_params["n_fft"] + 2, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+        self.reflection_pad = nn.ReflectionPad1d((1, 0))
+        self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32))
+        self.f0_predictor = f0_predictor
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+        self.m_source.remove_weight_norm()
+        for l in self.source_downs:
+            remove_weight_norm(l)
+        for l in self.source_resblocks:
+            l.remove_weight_norm()
+
+    def _stft(self, x):
+        spec = torch.stft(
+            x,
+            self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(x.device),
+            return_complex=True)
+        spec = torch.view_as_real(spec)  # [B, F, TT, 2]
+        return spec[..., 0], spec[..., 1]
+
+    def _istft(self, magnitude, phase):
+        magnitude = torch.clip(magnitude, max=1e2)
+        real = magnitude * torch.cos(phase)
+        img = magnitude * torch.sin(phase)
+        inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"],
+                                        self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device))
+        return inverse_transform
+
+    def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor:
+        s_stft_real, s_stft_imag = self._stft(s.squeeze(1))
+        s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1)
+
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, self.lrelu_slope)
+            x = self.ups[i](x)
+
+            if i == self.num_upsamples - 1:
+                x = self.reflection_pad(x)
+
+            # fusion
+            si = self.source_downs[i](s_stft)
+            si = self.source_resblocks[i](si)
+            x = x + si
+
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :])
+        phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :])  # actually, sin is redundancy
+
+        x = self._istft(magnitude, phase)
+        x = torch.clamp(x, -self.audio_limit, self.audio_limit)
+        return x
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        speech_feat = batch['speech_feat'].transpose(1, 2).to(device)
+        # mel->f0
+        f0 = self.f0_predictor(speech_feat)
+        # f0->source
+        s = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
+        s, _, _ = self.m_source(s)
+        s = s.transpose(1, 2)
+        # mel+source->speech
+        generated_speech = self.decode(x=speech_feat, s=s)
+        return generated_speech, f0
+
+    @torch.inference_mode()
+    def inference(self, speech_feat: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor:
+        # mel->f0
+        f0 = self.f0_predictor(speech_feat)
+        # f0->source
+        s = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
+        s, _, _ = self.m_source(s)
+        s = s.transpose(1, 2)
+        # use cache_source to avoid glitch
+        if cache_source.shape[2] != 0:
+            s[:, :, :cache_source.shape[2]] = cache_source
+        generated_speech = self.decode(x=speech_feat, s=s)
+        return generated_speech, s
+
+
+class CausalHiFTGenerator(HiFTGenerator):
+    """
+    HiFTNet Generator: Neural Source Filter + ISTFTNet
+    https://arxiv.org/abs/2309.09493
+    """
+    def __init__(
+            self,
+            in_channels: int = 80,
+            base_channels: int = 512,
+            nb_harmonics: int = 8,
+            sampling_rate: int = 22050,
+            nsf_alpha: float = 0.1,
+            nsf_sigma: float = 0.003,
+            nsf_voiced_threshold: float = 10,
+            upsample_rates: List[int] = [8, 8],
+            upsample_kernel_sizes: List[int] = [16, 16],
+            istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4},
+            resblock_kernel_sizes: List[int] = [3, 7, 11],
+            resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+            source_resblock_kernel_sizes: List[int] = [7, 11],
+            source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]],
+            lrelu_slope: float = 0.1,
+            audio_limit: float = 0.99,
+            conv_pre_look_right: int = 4,
+            f0_predictor: torch.nn.Module = None,
+    ):
+        torch.nn.Module.__init__(self)
+
+        self.out_channels = 1
+        self.nb_harmonics = nb_harmonics
+        self.sampling_rate = sampling_rate
+        self.istft_params = istft_params
+        self.lrelu_slope = lrelu_slope
+        self.audio_limit = audio_limit
+
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=sampling_rate,
+            upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"],
+            harmonic_num=nb_harmonics,
+            sine_amp=nsf_alpha,
+            add_noise_std=nsf_sigma,
+            voiced_threshod=nsf_voiced_threshold,
+            sinegen_type='1' if self.sampling_rate == 22050 else '2',
+            causal=True)
+        self.upsample_rates = upsample_rates
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"])
+
+        self.conv_pre = weight_norm(
+            CausalConv1d(in_channels, base_channels, conv_pre_look_right + 1, 1, causal_type='right')
+        )
+
+        # Up
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    CausalConv1dUpsample(
+                        base_channels // (2**i),
+                        base_channels // (2**(i + 1)),
+                        k,
+                        u,
+                    )
+                )
+            )
+
+        # Down
+        self.source_downs = nn.ModuleList()
+        self.source_resblocks = nn.ModuleList()
+        downsample_rates = [1] + upsample_rates[::-1][:-1]
+        downsample_cum_rates = np.cumprod(downsample_rates)
+        for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)):
+            if u == 1:
+                self.source_downs.append(
+                    CausalConv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1, causal_type='left')
+                )
+            else:
+                self.source_downs.append(
+                    CausalConv1dDownSample(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u)
+                )
+
+            self.source_resblocks.append(
+                ResBlock(base_channels // (2 ** (i + 1)), k, d, causal=True)
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = base_channels // (2**(i + 1))
+            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(ResBlock(ch, k, d, causal=True))
+
+        self.conv_post = weight_norm(CausalConv1d(ch, istft_params["n_fft"] + 2, 7, 1, causal_type='left'))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+        self.reflection_pad = nn.ReflectionPad1d((1, 0))
+        self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32))
+        self.conv_pre_look_right = conv_pre_look_right
+        self.f0_predictor = f0_predictor
+
+    def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0), finalize: bool = True) -> torch.Tensor:
+        s_stft_real, s_stft_imag = self._stft(s.squeeze(1))
+        if finalize is True:
+            x = self.conv_pre(x)
+        else:
+            x = self.conv_pre(x[:, :, :-self.conv_pre_look_right], x[:, :, -self.conv_pre_look_right:])
+            s_stft_real = s_stft_real[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)]
+            s_stft_imag = s_stft_imag[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)]
+        s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, self.lrelu_slope)
+            x = self.ups[i](x)
+
+            if i == self.num_upsamples - 1:
+                x = self.reflection_pad(x)
+
+            # fusion
+            si = self.source_downs[i](s_stft)
+            si = self.source_resblocks[i](si)
+            x = x + si
+
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :])
+        phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :])  # actually, sin is redundancy
+
+        x = self._istft(magnitude, phase)
+        if finalize is False:
+            x = x[:, :-int(np.prod(self.upsample_rates) * self.istft_params['hop_len'])]
+        x = torch.clamp(x, -self.audio_limit, self.audio_limit)
+        return x
+
+    @torch.inference_mode()
+    def inference(self, speech_feat: torch.Tensor, finalize: bool = True) -> torch.Tensor:
+        # mel->f0 NOTE f0_predictor precision is crucial for causal inference, move self.f0_predictor to cpu if necessary
+        self.f0_predictor.to(torch.float64)
+        f0 = self.f0_predictor(speech_feat.to(torch.float64), finalize=finalize).to(speech_feat)
+        # f0->source
+        s = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
+        s, _, _ = self.m_source(s)
+        s = s.transpose(1, 2)
+        if finalize is True:
+            generated_speech = self.decode(x=speech_feat, s=s, finalize=finalize)
+        else:
+            generated_speech = self.decode(x=speech_feat[:, :, :-self.f0_predictor.condnet[0].causal_padding], s=s, finalize=finalize)
+        return generated_speech, s
+
+
+if __name__ == '__main__':
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    from hyperpyyaml import load_hyperpyyaml
+    with open('./pretrained_models/Fun-CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f:
+        configs = load_hyperpyyaml(f, overrides={'llm': None, 'flow': None})
+    model = configs['hift']
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model.to(device)
+    model.eval()
+    max_len, chunk_size, context_size = 300, 30, 8
+    mel = torch.rand(1, 80, max_len).to(device)
+    pred_gt, _ = model.inference(mel)
+    for i in range(0, max_len, chunk_size):
+        finalize = True if i + chunk_size + context_size >= max_len else False
+        pred_chunk, _ = model.inference(mel[:, :, : i + chunk_size + context_size], finalize=finalize)
+        pred_chunk = pred_chunk[:, i * 480:]
+        print((pred_gt[:, i * 480:i * 480 + pred_chunk.shape[1]] - pred_chunk).abs().max().item())

third_party/CosyVoice/cosyvoice/hifigan/hifigan.py

@@ -0,0 +1,67 @@
+from typing import Dict, Optional
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from matcha.hifigan.models import feature_loss, generator_loss, discriminator_loss
+from cosyvoice.utils.losses import tpr_loss, mel_loss
+
+
+class HiFiGan(nn.Module):
+    def __init__(self, generator, discriminator, mel_spec_transform,
+                 multi_mel_spectral_recon_loss_weight=45, feat_match_loss_weight=2.0,
+                 tpr_loss_weight=1.0, tpr_loss_tau=0.04):
+        super(HiFiGan, self).__init__()
+        self.generator = generator
+        self.discriminator = discriminator
+        self.mel_spec_transform = mel_spec_transform
+        self.multi_mel_spectral_recon_loss_weight = multi_mel_spectral_recon_loss_weight
+        self.feat_match_loss_weight = feat_match_loss_weight
+        self.tpr_loss_weight = tpr_loss_weight
+        self.tpr_loss_tau = tpr_loss_tau
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        if batch['turn'] == 'generator':
+            return self.forward_generator(batch, device)
+        else:
+            return self.forward_discriminator(batch, device)
+
+    def forward_generator(self, batch, device):
+        real_speech = batch['speech'].to(device)
+        pitch_feat = batch['pitch_feat'].to(device)
+        # 1. calculate generator outputs
+        generated_speech, generated_f0 = self.generator(batch, device)
+        # 2. calculate discriminator outputs
+        y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech)
+        # 3. calculate generator losses, feature loss, mel loss, tpr losses [Optional]
+        loss_gen, _ = generator_loss(y_d_gs)
+        loss_fm = feature_loss(fmap_rs, fmap_gs)
+        loss_mel = mel_loss(real_speech, generated_speech, self.mel_spec_transform)
+        if self.tpr_loss_weight != 0:
+            loss_tpr = tpr_loss(y_d_gs, y_d_rs, self.tpr_loss_tau)
+        else:
+            loss_tpr = torch.zeros(1).to(device)
+        loss_f0 = F.l1_loss(generated_f0, pitch_feat)
+        loss = loss_gen + self.feat_match_loss_weight * loss_fm + \
+            self.multi_mel_spectral_recon_loss_weight * loss_mel + \
+            self.tpr_loss_weight * loss_tpr + loss_f0
+        return {'loss': loss, 'loss_gen': loss_gen, 'loss_fm': loss_fm, 'loss_mel': loss_mel, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0}
+
+    def forward_discriminator(self, batch, device):
+        real_speech = batch['speech'].to(device)
+        # 1. calculate generator outputs
+        with torch.no_grad():
+            generated_speech, generated_f0 = self.generator(batch, device)
+        # 2. calculate discriminator outputs
+        y_d_rs, y_d_gs, fmap_rs, fmap_gs = self.discriminator(real_speech, generated_speech.detach())
+        # 3. calculate discriminator losses, tpr losses [Optional]
+        loss_disc, _, _ = discriminator_loss(y_d_rs, y_d_gs)
+        if self.tpr_loss_weight != 0:
+            loss_tpr = tpr_loss(y_d_rs, y_d_gs, self.tpr_loss_tau)
+        else:
+            loss_tpr = torch.zeros(1).to(device)
+        loss = loss_disc + self.tpr_loss_weight * loss_tpr
+        return {'loss': loss, 'loss_disc': loss_disc, 'loss_tpr': loss_tpr}

third_party/CosyVoice/cosyvoice/llm/llm.py

@@ -0,0 +1,710 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#               2025 Alibaba Inc (authors: Xiang Lyu, Yabin Li, Qihua, Shengqiang Li)
+#
+# 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, queue
+import random
+import time
+import threading
+from typing import Dict, Optional, Callable, List, Generator
+import numpy as np
+import torch
+from torch import nn
+import torch.nn.functional as F
+from transformers import Qwen2ForCausalLM
+from torch.nn.utils.rnn import pad_sequence, unpad_sequence
+from cosyvoice.utils.common import IGNORE_ID
+from cosyvoice.transformer.label_smoothing_loss import LabelSmoothingLoss
+from cosyvoice.utils.common import th_accuracy
+from cosyvoice.utils.file_utils import logging
+from cosyvoice.utils.mask import make_pad_mask
+from cosyvoice.utils.onnx import SpeechTokenExtractor, online_feature, onnx_path
+
+
+class TransformerLM(torch.nn.Module):
+    def __init__(
+            self,
+            text_encoder_input_size: int,
+            llm_input_size: int,
+            llm_output_size: int,
+            text_token_size: int,
+            speech_token_size: int,
+            text_encoder: torch.nn.Module,
+            llm: torch.nn.Module,
+            sampling: Callable,
+            length_normalized_loss: bool = True,
+            lsm_weight: float = 0.0,
+            spk_embed_dim: int = 192,
+    ):
+        super().__init__()
+        self.llm_input_size = llm_input_size
+        self.speech_token_size = speech_token_size
+        # 1. build text token inputs related modules
+        self.text_embedding = torch.nn.Embedding(text_token_size, text_encoder_input_size)
+        self.text_encoder = text_encoder
+        self.text_encoder_affine_layer = nn.Linear(
+            self.text_encoder.output_size(),
+            llm_input_size
+        )
+
+        # 2. build speech token language model related modules
+        self.sos = 0
+        self.task_id = 1
+        self.eos_token = self.speech_token_size
+        self.llm_embedding = torch.nn.Embedding(2, llm_input_size)
+        self.llm = llm
+        self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 1)
+        self.criterion_ce = LabelSmoothingLoss(
+            size=speech_token_size + 1,
+            padding_idx=IGNORE_ID,
+            smoothing=lsm_weight,
+            normalize_length=length_normalized_loss,
+        )
+
+        # 3. [Optional] build speech token related modules
+        self.speech_embedding = torch.nn.Embedding(speech_token_size, llm_input_size)
+        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, llm_input_size)
+
+        # 4. sampling method
+        self.sampling = sampling
+
+    def encode(
+            self,
+            text: torch.Tensor,
+            text_lengths: torch.Tensor,
+    ):
+        encoder_out, encoder_mask = self.text_encoder(text, text_lengths, decoding_chunk_size=1, num_decoding_left_chunks=-1)
+        encoder_out_lens = encoder_mask.squeeze(1).sum(1)
+        encoder_out = self.text_encoder_affine_layer(encoder_out)
+        return encoder_out, encoder_out_lens
+
+    def pad_unpad_sequence(self, sos_emb, embedding, text_token, text_token_len, task_id_emb, speech_token, speech_token_len):
+        text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True)
+        speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
+        lm_input = [torch.concat([sos_emb.squeeze(dim=0), embedding[i], text_token[i], task_id_emb.squeeze(dim=0), speech_token[i]], dim=0)
+                    for i in range(len(text_token))]
+        lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32)
+        lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID)
+        return lm_input, lm_input_len
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        """
+        Args:
+            text: (B, L, D)
+            text_lengths: (B,)
+            audio: (B, T, N) or (B, T)
+            audio_lengths: (B,)
+        """
+        text_token = batch['text_token'].to(device)
+        text_token_len = batch['text_token_len'].to(device)
+        speech_token = batch['speech_token'].to(device)
+        speech_token_len = batch['speech_token_len'].to(device)
+        embedding = batch['embedding'].to(device)
+
+        # 1. prepare llm_target
+        lm_target = [torch.tensor([IGNORE_ID] * (2 + text_token_len[i]) + speech_token[i, :speech_token_len[i]].tolist() +
+                                  [self.speech_token_size]) for i in range(text_token.size(0))]
+        lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID).to(device)
+
+        # 1. encode text_token
+        text_token = self.text_embedding(text_token)
+        text_token, text_token_len = self.encode(text_token, text_token_len)
+
+        # 2. embedding projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+        embedding = embedding.unsqueeze(1)
+
+        # 3. sos and task_id
+        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
+        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+
+        # 4. encode speech_token
+        speech_token = self.speech_embedding(speech_token)
+
+        # 5. unpad and pad
+        lm_input, lm_input_len = self.pad_unpad_sequence(sos_emb, embedding, text_token, text_token_len,
+                                                         task_id_emb, speech_token, speech_token_len)
+
+        # 6. run lm forward
+        lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
+        logits = self.llm_decoder(lm_output)
+        loss = self.criterion_ce(logits, lm_target)
+        acc = th_accuracy(logits.view(-1, self.speech_token_size + 1), lm_target, ignore_label=IGNORE_ID)
+        return {'loss': loss, 'acc': acc}
+
+    def sampling_ids(
+            self,
+            weighted_scores: torch.Tensor,
+            decoded_tokens: List,
+            sampling: int,
+            ignore_eos: bool = True,
+    ):
+        if ignore_eos is True:
+            weighted_scores[self.speech_token_size] = -float('inf')
+        top_ids = self.sampling(weighted_scores, decoded_tokens, sampling)
+        return top_ids
+
+    @torch.inference_mode()
+    def inference(
+            self,
+            text: torch.Tensor,
+            text_len: torch.Tensor,
+            prompt_text: torch.Tensor,
+            prompt_text_len: torch.Tensor,
+            prompt_speech_token: torch.Tensor,
+            prompt_speech_token_len: torch.Tensor,
+            embedding: torch.Tensor,
+            sampling: int = 25,
+            max_token_text_ratio: float = 20,
+            min_token_text_ratio: float = 2,
+            uuid: str = '',
+    ) -> Generator[torch.Tensor, None, None]:
+        device = text.device
+        text = torch.concat([prompt_text, text], dim=1)
+        text_len += prompt_text_len
+        text = self.text_embedding(text)
+
+        # 1. encode text
+        text, text_len = self.encode(text, text_len)
+
+        # 2. encode embedding
+        if embedding.shape[0] != 0:
+            embedding = F.normalize(embedding, dim=1)
+            embedding = self.spk_embed_affine_layer(embedding)
+            embedding = embedding.unsqueeze(dim=1)
+        else:
+            embedding = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device).to(text.dtype)
+
+        # 3. concat llm_input
+        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
+        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+        if prompt_speech_token_len != 0:
+            prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
+        else:
+            prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device)
+        lm_input = torch.concat([sos_emb, embedding, text, task_id_emb, prompt_speech_token_emb], dim=1)
+
+        # 4. cal min/max_length
+        min_len = int((text_len - prompt_text_len) * min_token_text_ratio)
+        max_len = int((text_len - prompt_text_len) * max_token_text_ratio)
+
+        # 5. step by step decode
+        out_tokens = []
+        offset = 0
+        att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device)
+        for i in range(max_len):
+            y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=offset, required_cache_size=-1,
+                                                                  att_cache=att_cache, cnn_cache=cnn_cache,
+                                                                  att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]),
+                                                                                                 device=lm_input.device)).to(torch.bool))
+            logp = self.llm_decoder(y_pred[:, -1].to(dtype=self.llm_decoder.weight.dtype)).log_softmax(dim=-1)
+            top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False)
+            if top_ids == self.eos_token:
+                break
+            # in stream mode, yield token one by one
+            yield top_ids
+            out_tokens.append(top_ids)
+            offset += lm_input.size(1)
+            lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
+
+
+class Qwen2Encoder(torch.nn.Module):
+    def __init__(self, pretrain_path):
+        super().__init__()
+        self.model = Qwen2ForCausalLM.from_pretrained(pretrain_path)
+
+    @property
+    def input_dtype(self):
+        return self.model.model.embed_tokens.weight.dtype
+
+    def forward(self, xs: torch.Tensor, xs_lens: torch.Tensor):
+        T = xs.size(1)
+        masks = ~make_pad_mask(xs_lens, T)
+        outs = self.model(
+            inputs_embeds=xs.to(dtype=self.input_dtype),
+            attention_mask=masks,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+        return outs.hidden_states[-1], masks.unsqueeze(1)
+
+    def forward_one_step(self, xs, masks, cache=None):
+        input_masks = masks[:, -1, :]
+        outs = self.model(
+            inputs_embeds=xs.to(dtype=self.input_dtype),
+            attention_mask=input_masks,
+            output_hidden_states=True,
+            return_dict=True,
+            use_cache=True,
+            past_key_values=cache,
+        )
+        xs = outs.hidden_states[-1]
+        new_cache = outs.past_key_values
+        return xs, new_cache
+
+
+class Qwen2LM(TransformerLM):
+    def __init__(
+            self,
+            llm_input_size: int,
+            llm_output_size: int,
+            speech_token_size: int,
+            llm: torch.nn.Module,
+            sampling: Callable,
+            length_normalized_loss: bool = True,
+            lsm_weight: float = 0.0,
+            mix_ratio: List[int] = [5, 15],
+    ):
+        torch.nn.Module.__init__(self)
+        self.llm_input_size = llm_input_size
+        self.llm_output_size = llm_output_size
+        self.speech_token_size = speech_token_size
+        # 2. build speech token language model related modules
+        self.sos = 0
+        self.task_id = 1
+        self.eos_token = speech_token_size
+        self.fill_token = speech_token_size + 2
+
+        self.llm_embedding = torch.nn.Embedding(2, llm_input_size)
+        self.llm = llm
+        self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 3)
+        self.criterion_ce = LabelSmoothingLoss(
+            size=speech_token_size + 3,
+            padding_idx=IGNORE_ID,
+            smoothing=lsm_weight,
+            normalize_length=length_normalized_loss,
+        )
+
+        # 3. [Optional] build speech token related modules
+        self.speech_embedding = torch.nn.Embedding(speech_token_size + 3, llm_input_size)
+
+        # 4. sampling method
+        self.sampling = sampling
+        self.mix_ratio = mix_ratio
+
+        # 5. vllm related
+        self.stop_token_ids = [speech_token_size + i for i in range(3)]
+        self.vllm_output_queue = {}
+        if online_feature is True:
+            self.speech_token_extractor = SpeechTokenExtractor(model_path=os.path.join(onnx_path, 'speech_tokenizer_v2.batch.onnx'))
+
+    def prepare_lm_input_target(self, sos_emb, text_token, text_token_emb, text_token_len, task_id_emb, speech_token, speech_token_emb, speech_token_len, instruct_token=None, instruct_token_emb=None, instruct_token_len=None):
+        lm_target, lm_input = [], []
+        text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True)
+        speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
+        text_token_emb = unpad_sequence(text_token_emb, text_token_len.cpu(), batch_first=True)
+        speech_token_emb = unpad_sequence(speech_token_emb, speech_token_len.cpu(), batch_first=True)
+        # NOTE add instruct_token in CosyVoice3
+        if instruct_token is not None and instruct_token_emb is not None and instruct_token_len is not None:
+            instruct_token = unpad_sequence(instruct_token, instruct_token_len.cpu(), batch_first=True)
+            instruct_token_emb = unpad_sequence(instruct_token_emb, instruct_token_len.cpu(), batch_first=True)
+        else:
+            instruct_token = [torch.empty(0).to(text_token[0])] * len(text_token)
+            instruct_token_emb = [torch.empty(0, 896).to(text_token_emb[0])] * len(text_token)
+            instruct_token_len = torch.zeros(len(text_token)).to(text_token_len)
+        for i in range(len(text_token)):
+            # bistream sequence
+            if random.random() < 0.5 and speech_token_len[i] / text_token_len[i] > self.mix_ratio[1] / self.mix_ratio[0]:
+                this_lm_target, this_lm_input = [IGNORE_ID], [sos_emb.squeeze(dim=0)]
+                this_lm_target += [IGNORE_ID] * instruct_token_len[i]
+                this_lm_input.append(instruct_token_emb[i])
+                for j in range(((text_token_len[i] + 1) / self.mix_ratio[0]).ceil().int().item()):
+                    this_text_token = text_token[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]].tolist()
+                    this_speech_token = speech_token[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]].tolist()
+                    if len(this_text_token) == self.mix_ratio[0]:
+                        assert len(this_speech_token) == self.mix_ratio[1]
+                        this_lm_target += [IGNORE_ID] * (self.mix_ratio[0] - 1)
+                        this_lm_target += this_speech_token
+                        this_lm_target.append(self.fill_token)
+                        this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]])
+                        this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]])
+                    else:
+                        this_lm_target += [-1] * len(this_text_token)
+                        this_lm_target += speech_token[i][j * self.mix_ratio[1]:].tolist()
+                        this_lm_target.append(self.eos_token)
+                        this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]:])
+                        this_lm_input.append(task_id_emb.squeeze(dim=0))
+                        this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]:])
+                this_lm_target, this_lm_input = torch.tensor(this_lm_target), torch.concat(this_lm_input, dim=0)
+            # unistream sequence
+            else:
+                this_lm_target = torch.tensor([IGNORE_ID] * (1 + instruct_token_len[i] + text_token_len[i]) + speech_token[i].tolist() + [self.eos_token])
+                this_lm_input = torch.concat([sos_emb.squeeze(dim=0), instruct_token_emb[i], text_token_emb[i], task_id_emb.squeeze(dim=0), speech_token_emb[i]], dim=0)
+            lm_target.append(this_lm_target)
+            lm_input.append(this_lm_input)
+        lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32)
+        lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID)
+        lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID)
+        return lm_target, lm_input, lm_input_len
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        """
+        Args:
+            text: (B, L, D)
+            text_lengths: (B,)
+            audio: (B, T, N) or (B, T)
+            audio_lengths: (B,)
+        """
+        # 1. encode text_token
+        text_token = batch['text_token'].to(device)
+        text_token_len = batch['text_token_len'].to(device)
+        text_token_emb = self.llm.model.model.embed_tokens(text_token)
+
+        # 2. encode speech_token
+        if 'speech_token' not in batch:
+            speech_token, speech_token_len = self.speech_token_extractor.inference(batch['whisper_feat'], batch['whisper_feat_len'], device)
+        else:
+            speech_token = batch['speech_token'].to(device)
+            speech_token_len = batch['speech_token_len'].to(device)
+        speech_token_emb = self.speech_embedding(speech_token)
+
+        # 3. sos and task_id
+        if self.__class__.__name__ == 'CosyVoice3LM':
+            sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1)
+            task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1)
+        elif self.__class__.__name__ == 'Qwen2LM':
+            sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
+            task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+        else:
+            raise ValueError
+
+        # 4. prepare llm_input/target
+        if self.__class__.__name__ == 'CosyVoice3LM':
+            instruct_token = batch['instruct_token'].to(device)
+            instruct_token_len = batch['instruct_token_len'].to(device)
+            instruct_token_emb = self.llm.model.model.embed_tokens(instruct_token)
+            lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token, text_token_emb, text_token_len, task_id_emb,
+                                                                             speech_token, speech_token_emb, speech_token_len, instruct_token, instruct_token_emb, instruct_token_len)
+        elif self.__class__.__name__ == 'Qwen2LM':
+            lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token, text_token_emb, text_token_len, task_id_emb,
+                                                                             speech_token, speech_token_emb, speech_token_len)
+        else:
+            raise ValueError
+        lm_target = lm_target.to(device)
+
+        # 4. run lm forward
+        lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
+        logits = self.llm_decoder(lm_output)
+        loss = self.criterion_ce(logits, lm_target.to(device))
+        acc = th_accuracy(logits.view(-1, self.llm_decoder.out_features), lm_target, ignore_label=IGNORE_ID)
+        return {'loss': loss, 'acc': acc}
+
+    def forward_dpo(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        text_token = batch['text_token'].to(device)
+        text_token_len = batch['text_token_len'].to(device)
+        speech_token = batch['speech_token'].to(device)
+        speech_token_len = batch['speech_token_len'].to(device)
+        reject_speech_token = batch['reject_speech_token'].to(device)
+        reject_speech_token_len = batch['reject_speech_token_len'].to(device)
+
+        # 1. encode text_token
+        text_token_emb = self.llm.model.model.embed_tokens(text_token)
+
+        # 3. sos and task_id
+        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
+        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+
+        # 2. encode speech_token
+        speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
+        reject_speech_token = unpad_sequence(reject_speech_token, reject_speech_token_len.cpu(), batch_first=True)
+        speech_token_combined = speech_token + reject_speech_token
+        speech_token_combined = pad_sequence(speech_token_combined, batch_first=True, padding_value=0)
+        speech_token_combined_len = torch.concat([speech_token_len, reject_speech_token_len], dim=0)
+        speech_token_combined_emb = self.speech_embedding(speech_token_combined)
+
+        # 3. prepare llm_input/target
+        lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token.repeat(2, 1), text_token_emb.repeat(2, 1, 1), text_token_len.repeat(2),
+                                                                         task_id_emb, speech_token_combined, speech_token_combined_emb, speech_token_combined_len)
+        lm_target = lm_target.to(device)
+
+        # 4. run lm forward
+        lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
+        logits = self.llm_decoder(lm_output)
+        chosen_logits = logits[:text_token.shape[0]]
+        rejected_logits = logits[text_token.shape[0]:]
+        chosen_lm_target = lm_target[:text_token.shape[0]]
+        rejected_lm_target = lm_target[text_token.shape[0]:]
+        loss = self.criterion_ce(chosen_logits, chosen_lm_target.to(device))
+        acc = th_accuracy(chosen_logits.view(-1, self.speech_token_size + 3), chosen_lm_target, ignore_label=IGNORE_ID)
+
+        # 5. calculate dpo logits
+        chosen_lm_mask = chosen_lm_target == IGNORE_ID
+        rejected_lm_mask = rejected_lm_target == IGNORE_ID
+        chosen_logps = torch.gather(chosen_logits.log_softmax(dim=-1), dim=2, index=chosen_lm_target.masked_fill(chosen_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
+        rejected_logps = torch.gather(rejected_logits.log_softmax(dim=-1), dim=2, index=rejected_lm_target.masked_fill(rejected_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
+        chosen_logps = (chosen_logps * chosen_lm_mask).sum(dim=-1) / chosen_lm_mask.sum(dim=-1)
+        rejected_logps = (rejected_logps * rejected_lm_mask).sum(dim=-1) / rejected_lm_mask.sum(dim=-1)
+        return {'loss': loss, 'acc': acc, 'chosen_logps': chosen_logps, 'rejected_logps': rejected_logps}
+
+    @torch.inference_mode()
+    def inference(
+            self,
+            text: torch.Tensor,
+            text_len: torch.Tensor,
+            prompt_text: torch.Tensor,
+            prompt_text_len: torch.Tensor,
+            prompt_speech_token: torch.Tensor,
+            prompt_speech_token_len: torch.Tensor,
+            embedding: torch.Tensor,
+            sampling: int = 25,
+            max_token_text_ratio: float = 20,
+            min_token_text_ratio: float = 2,
+            uuid: str = '',
+    ) -> Generator[torch.Tensor, None, None]:
+        device = text.device
+        text = torch.concat([prompt_text, text], dim=1)
+        text_len += prompt_text_len
+        text_emb = self.llm.model.model.embed_tokens(text)
+        if self.__class__.__name__ == 'CosyVoice3LM':
+            # NOTE temporary hardcode, 151646 is <|endofprompt|> token
+            assert 151646 in text, '<|endofprompt|> not detected in CosyVoice3 text or prompt_text, check your input!'
+
+        # 3. concat llm_input
+        if self.__class__.__name__ == 'CosyVoice3LM':
+            sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1)
+            task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1)
+        elif self.__class__.__name__ == 'Qwen2LM':
+            sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
+            task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+        else:
+            raise ValueError
+        if prompt_speech_token_len != 0:
+            prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
+        else:
+            prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text_emb.dtype).to(device)
+        lm_input = torch.concat([sos_emb, text_emb, task_id_emb, prompt_speech_token_emb], dim=1)
+
+        # 4. cal min/max_length
+        min_len = int((text_len - prompt_text_len) * min_token_text_ratio)
+        max_len = int((text_len - prompt_text_len) * max_token_text_ratio)
+
+        # 5. step by step decode
+        for token in self.inference_wrapper(lm_input, sampling, min_len, max_len, uuid):
+            yield token
+
+    @torch.inference_mode()
+    def inference_wrapper(self, lm_input, sampling, min_len, max_len, uuid):
+        if hasattr(self, 'vllm'):
+            from vllm import SamplingParams, RequestOutput
+            sampling_params = SamplingParams(top_k=sampling,
+                                             stop_token_ids=self.stop_token_ids,
+                                             min_tokens=min_len,
+                                             max_tokens=max_len)
+            with self.lock:
+                self.vllm.add_request(uuid, {"prompt_embeds": lm_input.squeeze(0).to(torch.bfloat16).to(lm_input.device)}, sampling_params)
+                self.vllm_output_queue[uuid] = queue.Queue()
+            out_tokens = []
+            while True:
+                with self.lock:
+                    if self.vllm_output_queue[uuid].empty() is True:
+                        request_outputs: List[RequestOutput] = self.vllm.step()
+                        for request_output in request_outputs:
+                            top_ids = list(request_output.outputs[0].token_ids)[-1]
+                            self.vllm_output_queue[request_output.request_id].put(top_ids)
+                if self.vllm_output_queue[uuid].empty() is False:
+                    top_ids = self.vllm_output_queue[uuid].get()
+                    if top_ids in self.stop_token_ids:
+                        break
+                    # in stream mode, yield token one by one
+                    yield top_ids
+                    out_tokens.append(top_ids)
+                    if len(out_tokens) == max_len:
+                        break
+                time.sleep(0.001)
+            with self.lock:
+                self.vllm_output_queue.pop(uuid)
+        else:
+            out_tokens = []
+            cache = None
+            for i in range(max_len):
+                y_pred, cache = self.llm.forward_one_step(lm_input,
+                                                          masks=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), device=lm_input.device)).to(torch.bool),
+                                                          cache=cache)
+                logp = self.llm_decoder(y_pred[:, -1].to(dtype=self.llm_decoder.weight.dtype)).log_softmax(dim=-1)
+                top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False)
+                if top_ids in self.stop_token_ids:
+                    break
+                # in stream mode, yield token one by one
+                yield top_ids
+                out_tokens.append(top_ids)
+                lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
+
+    @torch.inference_mode()
+    def inference_bistream(
+            self,
+            text: Generator,
+            prompt_text: torch.Tensor,
+            prompt_text_len: torch.Tensor,
+            prompt_speech_token: torch.Tensor,
+            prompt_speech_token_len: torch.Tensor,
+            embedding: torch.Tensor,
+            sampling: int = 25,
+            max_token_text_ratio: float = 20,
+            min_token_text_ratio: float = 2,
+    ) -> Generator[torch.Tensor, None, None]:
+
+        device = prompt_text.device
+        # 1. prepare input
+        if self.__class__.__name__ == 'CosyVoice3LM':
+            sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1)
+            task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1)
+        elif self.__class__.__name__ == 'Qwen2LM':
+            sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
+            task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+        else:
+            raise ValueError
+        if prompt_speech_token_len != 0:
+            prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
+        else:
+            prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=prompt_text.dtype).to(device)
+        lm_input = torch.concat([sos_emb], dim=1)
+
+        # 2. iterate text
+        out_tokens = []
+        cache = None
+        # NOTE init prompt_text as text_cache as it is basically impossible prompt_speech_token/prompt_text < 15/5
+        if self.__class__.__name__ == 'CosyVoice3LM':
+            # NOTE temporary hardcode, 151646 is <|endofprompt|> token
+            assert 151646 in prompt_text, '<|endofprompt|> not detected in CosyVoice3 prompt_text, check your input!'
+            eop_index = prompt_text.flatten().tolist().index(151646)
+            lm_input = torch.concat([lm_input, self.llm.model.model.embed_tokens(prompt_text[:, :eop_index + 1])], dim=1)
+            prompt_text = prompt_text[:, eop_index + 1:]
+        text_cache = self.llm.model.model.embed_tokens(prompt_text)
+        next_fill_index = (int(prompt_speech_token.shape[1] / self.mix_ratio[1]) + 1) * self.mix_ratio[1] - prompt_speech_token.shape[1]
+        for this_text in text:
+            text_cache = torch.concat([text_cache, self.llm.model.model.embed_tokens(this_text)], dim=1)
+            # prompt_speech_token_emb not empty, try append to lm_input
+            while prompt_speech_token_emb.size(1) != 0:
+                if text_cache.size(1) >= self.mix_ratio[0]:
+                    lm_input_text, lm_input_speech = text_cache[:, :self.mix_ratio[0]], prompt_speech_token_emb[:, :self.mix_ratio[1]]
+                    logging.info('append {} text token {} speech token'.format(lm_input_text.size(1), lm_input_speech.size(1)))
+                    lm_input = torch.concat([lm_input, lm_input_text, lm_input_speech], dim=1)
+                    text_cache, prompt_speech_token_emb = text_cache[:, self.mix_ratio[0]:], prompt_speech_token_emb[:, self.mix_ratio[1]:]
+                else:
+                    logging.info('not enough text token to decode, wait for more')
+                    break
+            # no prompt_speech_token_emb remain, can decode some speech token
+            if prompt_speech_token_emb.size(1) == 0:
+                if (len(out_tokens) != 0 and out_tokens[-1] == self.fill_token) or (len(out_tokens) == 0 and lm_input.size(1) == 1):
+                    logging.info('get fill token, need to append more text token')
+                    if text_cache.size(1) >= self.mix_ratio[0]:
+                        lm_input_text = text_cache[:, :self.mix_ratio[0]]
+                        logging.info('append {} text token'.format(lm_input_text.size(1)))
+                        if len(out_tokens) != 0 and out_tokens[-1] == self.fill_token:
+                            lm_input = lm_input_text
+                        else:
+                            lm_input = torch.concat([lm_input, lm_input_text], dim=1)
+                        text_cache = text_cache[:, self.mix_ratio[0]:]
+                    else:
+                        logging.info('not enough text token to decode, wait for more')
+                        continue
+                while True:
+                    seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2)
+                    y_pred, cache = self.llm.forward_one_step(lm_input,
+                                                              masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool),
+                                                              cache=cache)
+                    logp = self.llm_decoder(y_pred[:, -1].to(dtype=self.llm_decoder.weight.dtype)).log_softmax(dim=-1)
+                    if next_fill_index != -1 and len(out_tokens) == next_fill_index:
+                        top_ids = self.fill_token
+                        next_fill_index += (self.mix_ratio[1] + 1)
+                    else:
+                        top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True)
+                    if top_ids == self.fill_token:
+                        next_fill_index = len(out_tokens) + self.mix_ratio[1] + 1
+                        logging.info('fill_token index {} next fill_token index {}'.format(len(out_tokens), next_fill_index))
+                    out_tokens.append(top_ids)
+                    if top_ids >= self.speech_token_size:
+                        if top_ids == self.fill_token:
+                            break
+                        else:
+                            raise ValueError('should not get token {}'.format(top_ids))
+                    yield top_ids
+                    lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
+
+        # 3. final decode
+        lm_input = torch.concat([lm_input, text_cache, task_id_emb], dim=1)
+        logging.info('no more text token, decode until met eos')
+        while True:
+            seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2)
+            y_pred, cache = self.llm.forward_one_step(lm_input,
+                                                      masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool),
+                                                      cache=cache)
+            logp = self.llm_decoder(y_pred[:, -1].to(dtype=self.llm_decoder.weight.dtype)).log_softmax(dim=-1)
+            top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=False)
+            out_tokens.append(top_ids)
+            if top_ids >= self.speech_token_size:
+                if top_ids == self.eos_token:
+                    break
+                else:
+                    raise ValueError('should not get token {}'.format(top_ids))
+            # in stream mode, yield token one by one
+            yield top_ids
+            lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
+
+
+class CosyVoice3LM(Qwen2LM):
+    def __init__(
+            self,
+            llm_input_size: int,
+            llm_output_size: int,
+            speech_token_size: int,
+            llm: torch.nn.Module,
+            sampling: Callable,
+            length_normalized_loss: bool = True,
+            lsm_weight: float = 0.0,
+            mix_ratio: List[int] = [5, 15],
+    ):
+        torch.nn.Module.__init__(self)
+        self.llm_input_size = llm_input_size
+        self.llm_output_size = llm_output_size
+        self.speech_token_size = speech_token_size
+        # 2. build speech token language model related modules
+        self.sos = speech_token_size + 0
+        self.eos_token = speech_token_size + 1
+        self.task_id = speech_token_size + 2
+        self.fill_token = speech_token_size + 3
+
+        self.llm = llm
+        self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 200, bias=False)
+        self.criterion_ce = LabelSmoothingLoss(
+            size=speech_token_size + 200,
+            padding_idx=IGNORE_ID,
+            smoothing=lsm_weight,
+            normalize_length=length_normalized_loss,
+        )
+
+        # 3. [Optional] build speech token related modules
+        self.speech_embedding = torch.nn.Embedding(speech_token_size + 200, llm_input_size)
+
+        # 4. sampling method
+        self.sampling = sampling
+        self.mix_ratio = mix_ratio
+
+        # 5. vllm related
+        self.stop_token_ids = [speech_token_size + i for i in range(200)]
+        self.vllm_output_queue = {}
+        if online_feature is True:
+            self.speech_token_extractor = SpeechTokenExtractor(model_path=os.path.join(onnx_path, 'speech_tokenizer_v3.batch.onnx'))

third_party/CosyVoice/cosyvoice/tokenizer/tokenizer.py

@@ -0,0 +1,327 @@
+import base64
+import os
+from functools import lru_cache
+from typing import Optional
+import torch
+from transformers import AutoTokenizer
+from whisper.tokenizer import Tokenizer
+
+import tiktoken
+
+LANGUAGES = {
+    "en": "english",
+    "zh": "chinese",
+    "de": "german",
+    "es": "spanish",
+    "ru": "russian",
+    "ko": "korean",
+    "fr": "french",
+    "ja": "japanese",
+    "pt": "portuguese",
+    "tr": "turkish",
+    "pl": "polish",
+    "ca": "catalan",
+    "nl": "dutch",
+    "ar": "arabic",
+    "sv": "swedish",
+    "it": "italian",
+    "id": "indonesian",
+    "hi": "hindi",
+    "fi": "finnish",
+    "vi": "vietnamese",
+    "he": "hebrew",
+    "uk": "ukrainian",
+    "el": "greek",
+    "ms": "malay",
+    "cs": "czech",
+    "ro": "romanian",
+    "da": "danish",
+    "hu": "hungarian",
+    "ta": "tamil",
+    "no": "norwegian",
+    "th": "thai",
+    "ur": "urdu",
+    "hr": "croatian",
+    "bg": "bulgarian",
+    "lt": "lithuanian",
+    "la": "latin",
+    "mi": "maori",
+    "ml": "malayalam",
+    "cy": "welsh",
+    "sk": "slovak",
+    "te": "telugu",
+    "fa": "persian",
+    "lv": "latvian",
+    "bn": "bengali",
+    "sr": "serbian",
+    "az": "azerbaijani",
+    "sl": "slovenian",
+    "kn": "kannada",
+    "et": "estonian",
+    "mk": "macedonian",
+    "br": "breton",
+    "eu": "basque",
+    "is": "icelandic",
+    "hy": "armenian",
+    "ne": "nepali",
+    "mn": "mongolian",
+    "bs": "bosnian",
+    "kk": "kazakh",
+    "sq": "albanian",
+    "sw": "swahili",
+    "gl": "galician",
+    "mr": "marathi",
+    "pa": "punjabi",
+    "si": "sinhala",
+    "km": "khmer",
+    "sn": "shona",
+    "yo": "yoruba",
+    "so": "somali",
+    "af": "afrikaans",
+    "oc": "occitan",
+    "ka": "georgian",
+    "be": "belarusian",
+    "tg": "tajik",
+    "sd": "sindhi",
+    "gu": "gujarati",
+    "am": "amharic",
+    "yi": "yiddish",
+    "lo": "lao",
+    "uz": "uzbek",
+    "fo": "faroese",
+    "ht": "haitian creole",
+    "ps": "pashto",
+    "tk": "turkmen",
+    "nn": "nynorsk",
+    "mt": "maltese",
+    "sa": "sanskrit",
+    "lb": "luxembourgish",
+    "my": "myanmar",
+    "bo": "tibetan",
+    "tl": "tagalog",
+    "mg": "malagasy",
+    "as": "assamese",
+    "tt": "tatar",
+    "haw": "hawaiian",
+    "ln": "lingala",
+    "ha": "hausa",
+    "ba": "bashkir",
+    "jw": "javanese",
+    "su": "sundanese",
+    "yue": "cantonese",
+    "minnan": "minnan",
+    "wuyu": "wuyu",
+    "dialect": "dialect",
+    "zh/en": "zh/en",
+    "en/zh": "en/zh",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+    **{language: code for code, language in LANGUAGES.items()},
+    "burmese": "my",
+    "valencian": "ca",
+    "flemish": "nl",
+    "haitian": "ht",
+    "letzeburgesch": "lb",
+    "pushto": "ps",
+    "panjabi": "pa",
+    "moldavian": "ro",
+    "moldovan": "ro",
+    "sinhalese": "si",
+    "castilian": "es",
+    "mandarin": "zh",
+}
+
+AUDIO_EVENT = {
+    "ASR": "ASR",
+    "AED": "AED",
+    "SER": "SER",
+    "Speech": "Speech",
+    "/Speech": "/Speech",
+    "BGM": "BGM",
+    "/BGM": "/BGM",
+    "Laughter": "Laughter",
+    "/Laughter": "/Laughter",
+    "Applause": "Applause",
+    "/Applause": "/Applause",
+}
+
+EMOTION = {
+    "HAPPY": "HAPPY",
+    "SAD": "SAD",
+    "ANGRY": "ANGRY",
+    "NEUTRAL": "NEUTRAL",
+}
+
+TTS_Vocal_Token = {
+    "TTS/B": "TTS/B",
+    "TTS/O": "TTS/O",
+    "TTS/Q": "TTS/Q",
+    "TTS/A": "TTS/A",
+    "TTS/CO": "TTS/CO",
+    "TTS/CL": "TTS/CL",
+    "TTS/H": "TTS/H",
+    **{f"TTS/SP{i:02d}": f"TTS/SP{i:02d}" for i in range(1, 14)}
+}
+
+
+@lru_cache(maxsize=None)
+def get_encoding(name: str = "gpt2", num_languages: int = 99):
+    vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken")
+    ranks = {
+        base64.b64decode(token): int(rank)
+        for token, rank in (line.split() for line in open(vocab_path) if line)
+    }
+    n_vocab = len(ranks)
+    special_tokens = {}
+
+    specials = [
+        "<|endoftext|>",
+        "<|startoftranscript|>",
+        *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
+        *[f"<|{audio_event}|>" for audio_event in list(AUDIO_EVENT.keys())],
+        *[f"<|{emotion}|>" for emotion in list(EMOTION.keys())],
+        "<|translate|>",
+        "<|transcribe|>",
+        "<|startoflm|>",
+        "<|startofprev|>",
+        "<|nospeech|>",
+        "<|notimestamps|>",
+        *[f"<|SPECIAL_TOKEN_{i}|>" for i in range(1, 31)],        # register special tokens for ASR
+        *[f"<|{tts}|>" for tts in list(TTS_Vocal_Token.keys())],  # register special tokens for TTS
+        *[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
+    ]
+
+    for token in specials:
+        special_tokens[token] = n_vocab
+        n_vocab += 1
+
+    return tiktoken.Encoding(
+        name=os.path.basename(vocab_path),
+        explicit_n_vocab=n_vocab,
+        pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+        mergeable_ranks=ranks,
+        special_tokens=special_tokens,
+    )
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+    multilingual: bool,
+    *,
+    num_languages: int = 99,
+    language: Optional[str] = None,
+    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
+) -> Tokenizer:
+    if language is not None:
+        language = language.lower()
+        if language not in LANGUAGES:
+            if language in TO_LANGUAGE_CODE:
+                language = TO_LANGUAGE_CODE[language]
+            else:
+                raise ValueError(f"Unsupported language: {language}")
+
+    if multilingual:
+        encoding_name = "multilingual_zh_ja_yue_char_del"
+        language = language or "en"
+        task = task or "transcribe"
+    else:
+        encoding_name = "gpt2"
+        language = None
+        task = None
+
+    encoding = get_encoding(name=encoding_name, num_languages=num_languages)
+
+    return Tokenizer(
+        encoding=encoding, num_languages=num_languages, language=language, task=task
+    )
+
+
+class CosyVoice2Tokenizer():
+    def __init__(self, token_path, skip_special_tokens=True):
+        super().__init__()
+        # NOTE: non-chat model, all these special tokens keep randomly initialized.
+        special_tokens = {
+            'eos_token': '<|endoftext|>',
+            'pad_token': '<|endoftext|>',
+            'additional_special_tokens': [
+                '<|im_start|>', '<|im_end|>', '<|endofprompt|>',
+                '[breath]', '<strong>', '</strong>', '[noise]',
+                '[laughter]', '[cough]', '[clucking]', '[accent]',
+                '[quick_breath]',
+                "<laughter>", "</laughter>",
+                "[hissing]", "[sigh]", "[vocalized-noise]",
+                "[lipsmack]", "[mn]"
+            ]
+        }
+        self.special_tokens = special_tokens
+        self.tokenizer = AutoTokenizer.from_pretrained(token_path)
+        self.tokenizer.add_special_tokens(special_tokens)
+        self.skip_special_tokens = skip_special_tokens
+
+    def encode(self, text, **kwargs):
+        tokens = self.tokenizer([text], return_tensors="pt")
+        tokens = tokens["input_ids"][0].cpu().tolist()
+        return tokens
+
+    def decode(self, tokens):
+        tokens = torch.tensor(tokens, dtype=torch.int64)
+        text = self.tokenizer.batch_decode([tokens], skip_special_tokens=self.skip_special_tokens)[0]
+        return text
+
+
+class CosyVoice3Tokenizer(CosyVoice2Tokenizer):
+    def __init__(self, token_path, skip_special_tokens=True):
+        # NOTE: non-chat model, all these special tokens keep randomly initialized.
+        special_tokens = {
+            'eos_token': '<|endoftext|>',
+            'pad_token': '<|endoftext|>',
+            'additional_special_tokens': [
+                '<|im_start|>', '<|im_end|>', '<|endofprompt|>',
+                '[breath]', '<strong>', '</strong>', '[noise]',
+                '[laughter]', '[cough]', '[clucking]', '[accent]',
+                '[quick_breath]',
+                "<laughter>", "</laughter>",
+                "[hissing]", "[sigh]", "[vocalized-noise]",
+                "[lipsmack]", "[mn]", "<|endofsystem|>",
+                "[AA]", "[AA0]", "[AA1]", "[AA2]", "[AE]", "[AE0]", "[AE1]", "[AE2]", "[AH]", "[AH0]", "[AH1]", "[AH2]",
+                "[AO]", "[AO0]", "[AO1]", "[AO2]", "[AW]", "[AW0]", "[AW1]", "[AW2]", "[AY]", "[AY0]", "[AY1]", "[AY2]",
+                "[B]", "[CH]", "[D]", "[DH]", "[EH]", "[EH0]", "[EH1]", "[EH2]", "[ER]", "[ER0]", "[ER1]", "[ER2]", "[EY]",
+                "[EY0]", "[EY1]", "[EY2]", "[F]", "[G]", "[HH]", "[IH]", "[IH0]", "[IH1]", "[IH2]", "[IY]", "[IY0]", "[IY1]",
+                "[IY2]", "[JH]", "[K]", "[L]", "[M]", "[N]", "[NG]", "[OW]", "[OW0]", "[OW1]", "[OW2]", "[OY]", "[OY0]",
+                "[OY1]", "[OY2]", "[P]", "[R]", "[S]", "[SH]", "[T]", "[TH]", "[UH]", "[UH0]", "[UH1]", "[UH2]", "[UW]",
+                "[UW0]", "[UW1]", "[UW2]", "[V]", "[W]", "[Y]", "[Z]", "[ZH]",
+                "[a]", "[ai]", "[an]", "[ang]", "[ao]", "[b]", "[c]", "[ch]", "[d]", "[e]", "[ei]", "[en]", "[eng]", "[f]",
+                "[g]", "[h]", "[i]", "[ian]", "[in]", "[ing]", "[iu]", "[ià]", "[iàn]", "[iàng]", "[iào]", "[iá]", "[ián]",
+                "[iáng]", "[iáo]", "[iè]", "[ié]", "[iòng]", "[ióng]", "[iù]", "[iú]", "[iā]", "[iān]", "[iāng]", "[iāo]",
+                "[iē]", "[iě]", "[iōng]", "[iū]", "[iǎ]", "[iǎn]", "[iǎng]", "[iǎo]", "[iǒng]", "[iǔ]", "[j]", "[k]", "[l]",
+                "[m]", "[n]", "[o]", "[ong]", "[ou]", "[p]", "[q]", "[r]", "[s]", "[sh]", "[t]", "[u]", "[uang]", "[ue]",
+                "[un]", "[uo]", "[uà]", "[uài]", "[uàn]", "[uàng]", "[uá]", "[uái]", "[uán]", "[uáng]", "[uè]", "[ué]", "[uì]",
+                "[uí]", "[uò]", "[uó]", "[uā]", "[uāi]", "[uān]", "[uāng]", "[uē]", "[uě]", "[uī]", "[uō]", "[uǎ]", "[uǎi]",
+                "[uǎn]", "[uǎng]", "[uǐ]", "[uǒ]", "[vè]", "[w]", "[x]", "[y]", "[z]", "[zh]", "[à]", "[ài]", "[àn]", "[àng]",
+                "[ào]", "[á]", "[ái]", "[án]", "[��ng]", "[áo]", "[è]", "[èi]", "[èn]", "[èng]", "[èr]", "[é]", "[éi]", "[én]",
+                "[éng]", "[ér]", "[ì]", "[ìn]", "[ìng]", "[í]", "[ín]", "[íng]", "[ò]", "[òng]", "[òu]", "[ó]", "[óng]", "[óu]",
+                "[ù]", "[ùn]", "[ú]", "[ún]", "[ā]", "[āi]", "[ān]", "[āng]", "[āo]", "[ē]", "[ēi]", "[ēn]", "[ēng]", "[ě]",
+                "[ěi]", "[ěn]", "[ěng]", "[ěr]", "[ī]", "[īn]", "[īng]", "[ō]", "[ōng]", "[ōu]", "[ū]", "[ūn]", "[ǎ]", "[ǎi]",
+                "[ǎn]", "[ǎng]", "[ǎo]", "[ǐ]", "[ǐn]", "[ǐng]", "[ǒ]", "[ǒng]", "[ǒu]", "[ǔ]", "[ǔn]", "[ǘ]", "[ǚ]", "[ǜ]"
+            ]
+        }
+        self.special_tokens = special_tokens
+        self.tokenizer = AutoTokenizer.from_pretrained(token_path)
+        self.tokenizer.add_special_tokens(special_tokens)
+        self.skip_special_tokens = skip_special_tokens
+
+
+@lru_cache(maxsize=None)
+def get_qwen_tokenizer(
+    token_path: str,
+    skip_special_tokens: bool,
+    version: str = 'cosyvoice2'
+):
+    if version == 'cosyvoice2':
+        return CosyVoice2Tokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens)
+    elif version == 'cosyvoice3':
+        return CosyVoice3Tokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens)
+    else:
+        raise ValueError