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+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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+# SPDX-License-Identifier: Apache-2.0
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+#
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+# Licensed under the Apache License, Version 2.0 (the "License");
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+# you may not use this file except in compliance with the License.
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+# You may obtain a copy of the License at
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+#
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+# http://www.apache.org/licenses/LICENSE-2.0
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+#
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+# Unless required by applicable law or agreed to in writing, software
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+# distributed under the License is distributed on an "AS IS" BASIS,
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+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+# See the License for the specific language governing permissions and
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+# limitations under the License.
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+""" Example Usage
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+ CUDA_VISIBLE_DEVICES=0 \
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+ python3 token2wav.py --enable-trt || exit 1
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+"""
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+import torch
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+# from flashcosyvoice.modules.flow import CausalMaskedDiffWithXvec
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+from flashcosyvoice.modules.hifigan import HiFTGenerator
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+from flashcosyvoice.utils.audio import mel_spectrogram
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+import torchaudio.compliance.kaldi as kaldi
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+import onnxruntime
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+import s3tokenizer
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+from torch.utils.data import DataLoader
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+from datasets import load_dataset
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+import torchaudio
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+import os
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+import logging
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+import argparse
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+import queue
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+import time
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+import numpy as np
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+from hyperpyyaml import load_hyperpyyaml
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+
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+
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+def fade_in_out(fade_in_mel: torch.Tensor, fade_out_mel: torch.Tensor, window: torch.Tensor):
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+ """perform fade_in_out in tensor style
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+ """
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+ mel_overlap_len = int(window.shape[0] / 2)
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+ fade_in_mel = fade_in_mel.clone()
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+ fade_in_mel[..., :mel_overlap_len] = \
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+ fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \
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+ fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:]
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+ return fade_in_mel
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+
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+
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+def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, dtype):
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+ import tensorrt as trt
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+ logging.info("Converting onnx to trt...")
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+ network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
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+ logger = trt.Logger(trt.Logger.INFO)
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+ builder = trt.Builder(logger)
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+ network = builder.create_network(network_flags)
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+ parser = trt.OnnxParser(network, logger)
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+ config = builder.create_builder_config()
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+ # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 32) # 4GB
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+ if dtype == torch.float16:
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+ config.set_flag(trt.BuilderFlag.FP16)
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+
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+ profile = builder.create_optimization_profile()
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+ # load onnx model
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+ with open(onnx_model, "rb") as f:
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+ if not parser.parse(f.read()):
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+ for error in range(parser.num_errors):
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+ print(parser.get_error(error))
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+ raise ValueError('failed to parse {}'.format(onnx_model))
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+ # set input shapes
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+ for i in range(len(trt_kwargs['input_names'])):
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+ profile.set_shape(trt_kwargs['input_names'][i], trt_kwargs['min_shape'][i], trt_kwargs['opt_shape'][i], trt_kwargs['max_shape'][i])
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+ if dtype == torch.float16:
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+ tensor_dtype = trt.DataType.HALF
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+ elif dtype == torch.bfloat16:
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+ tensor_dtype = trt.DataType.BF16
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+ elif dtype == torch.float32:
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+ tensor_dtype = trt.DataType.FLOAT
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+ else:
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+ raise ValueError('invalid dtype {}'.format(dtype))
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+ # set input and output data type
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+ for i in range(network.num_inputs):
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+ input_tensor = network.get_input(i)
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+ input_tensor.dtype = tensor_dtype
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+ for i in range(network.num_outputs):
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+ output_tensor = network.get_output(i)
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+ output_tensor.dtype = tensor_dtype
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+ config.add_optimization_profile(profile)
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+ engine_bytes = builder.build_serialized_network(network, config)
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+ # save trt engine
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+ with open(trt_model, "wb") as f:
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+ f.write(engine_bytes)
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+ logging.info("Succesfully convert onnx to trt...")
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+
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+
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+class TrtContextWrapper:
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+ def __init__(self, trt_engine, trt_concurrent=1, device='cuda:0'):
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+ self.trt_context_pool = queue.Queue(maxsize=trt_concurrent)
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+ self.trt_engine = trt_engine
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+ self.device = device
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+ for _ in range(trt_concurrent):
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+ trt_context = trt_engine.create_execution_context()
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+ trt_stream = torch.cuda.stream(torch.cuda.Stream(torch.device(device)))
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+ assert trt_context is not None, 'failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}'.format(trt_concurrent)
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+ self.trt_context_pool.put([trt_context, trt_stream])
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+ assert self.trt_context_pool.empty() is False, 'no avaialbe estimator context'
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+
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+ def acquire_estimator(self):
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+ return self.trt_context_pool.get(), self.trt_engine
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+
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+ def release_estimator(self, context, stream):
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+ self.trt_context_pool.put([context, stream])
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+
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+
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+class CosyVoice2_Token2Wav(torch.nn.Module):
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+ def __init__(self, model_dir: str, enable_trt: bool = False, device_id: int = 0, streaming: bool = False, dtype: torch.dtype = torch.float16):
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+ super().__init__()
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+ self.device_id = device_id
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+ self.device = f"cuda:{device_id}"
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+ with open(f"{model_dir}/flow.yaml", "r") as f:
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+ configs = load_hyperpyyaml(f)
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+ self.flow = configs['flow']
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+
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+ self.dtype = dtype
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+ self.flow.to(self.dtype)
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+
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+ self.flow.load_state_dict(torch.load(f"{model_dir}/flow.pt", map_location="cpu", weights_only=True), strict=True)
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+ self.flow.to(self.device).eval()
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+
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+ self.hift = HiFTGenerator()
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+ hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(f"{model_dir}/hift.pt", map_location="cpu", weights_only=True).items()}
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+ self.hift.load_state_dict(hift_state_dict, strict=True)
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+ self.hift.to(self.device).eval()
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+
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+ option = onnxruntime.SessionOptions()
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+ option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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+ option.intra_op_num_threads = 1
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+ self.spk_model = onnxruntime.InferenceSession(
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+ f"{model_dir}/campplus.onnx", sess_options=option,
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+ providers=["CPUExecutionProvider"])
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+ self.audio_tokenizer = s3tokenizer.load_model(f"{model_dir}/speech_tokenizer_v2_25hz.onnx").to(self.device).eval()
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+
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+ gpu = "l20"
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+ if enable_trt:
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+ if streaming:
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+ self.load_trt(
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+ f'{model_dir}/flow.decoder.estimator.{self.dtype}.dynamic_batch.chunk.{gpu}.plan',
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+ f'{model_dir}/flow.decoder.estimator.chunk.fp32.dynamic_batch.simplify.onnx',
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+ 1,
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+ self.dtype, streaming
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+ )
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+ else:
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+ self.load_trt(
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+ f'{model_dir}/flow.decoder.estimator.{self.dtype}.dynamic_batch.{gpu}.plan',
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+ f'{model_dir}/flow.decoder.estimator.fp32.dynamic_batch.onnx',
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+ 1,
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+ self.dtype
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+ )
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+ self.load_spk_trt(
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+ f'{model_dir}/campplus.{gpu}.fp32.trt',
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+ f'{model_dir}/campplus.onnx',
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+ 1,
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+ False
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+ )
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+
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+ self.streaming_flow_cache = {}
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+ self.speaker_cache = {}
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+
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+ self.mel_cache_len = 8 # hard-coded, 160ms
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+ self.source_cache_len = int(self.mel_cache_len * 480) # 50hz mel -> 24kHz wave
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+ self.speech_window = torch.from_numpy(np.hamming(2 * self.source_cache_len)).cuda()
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+
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+ # hifigan cache for streaming tts
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+ self.hift_cache_dict = {}
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+
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+ def forward_spk_embedding(self, spk_feat):
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+ if isinstance(self.spk_model, onnxruntime.InferenceSession):
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+ return self.spk_model.run(
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+ None, {self.spk_model.get_inputs()[0].name: spk_feat.unsqueeze(dim=0).cpu().numpy()}
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+ )[0].flatten().tolist()
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+ else:
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+ [spk_model, stream], trt_engine = self.spk_model.acquire_estimator()
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+ # NOTE need to synchronize when switching stream
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+ with torch.cuda.device(self.device_id):
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+ torch.cuda.current_stream().synchronize()
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+ spk_feat = spk_feat.unsqueeze(dim=0).to(self.device)
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+ batch_size = spk_feat.size(0)
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+
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+ with stream:
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+ spk_model.set_input_shape('input', (batch_size, spk_feat.size(1), 80))
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+ output_tensor = torch.empty((batch_size, 192), device=spk_feat.device)
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+
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+ data_ptrs = [spk_feat.contiguous().data_ptr(),
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+ output_tensor.contiguous().data_ptr()]
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+ for i, j in enumerate(data_ptrs):
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+
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+ spk_model.set_tensor_address(trt_engine.get_tensor_name(i), j)
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+ # run trt engine
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+ assert spk_model.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True
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+ torch.cuda.current_stream().synchronize()
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+ self.spk_model.release_estimator(spk_model, stream)
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+
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+ return output_tensor.cpu().numpy().flatten().tolist()
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+
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+ def load_spk_trt(self, spk_model, spk_onnx_model, trt_concurrent=1, fp16=True):
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+ if not os.path.exists(spk_model) or os.path.getsize(spk_model) == 0:
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+ trt_kwargs = self.get_spk_trt_kwargs()
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+ convert_onnx_to_trt(spk_model, trt_kwargs, spk_onnx_model, torch.float32)
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+ import tensorrt as trt
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+ with open(spk_model, 'rb') as f:
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+ spk_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
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+ assert spk_engine is not None, 'failed to load trt {}'.format(spk_model)
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+ self.spk_model = TrtContextWrapper(spk_engine, trt_concurrent=trt_concurrent, device=self.device)
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+
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+ def get_spk_trt_kwargs(self):
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+ min_shape = [(1, 4, 80)]
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+ opt_shape = [(1, 500, 80)]
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+ max_shape = [(1, 3000, 80)]
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+ input_names = ["input"]
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+ return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
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+
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+ def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent=1, dtype=torch.float16, streaming=False):
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+ assert torch.cuda.is_available(), 'tensorrt only supports gpu!'
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+ if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0:
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+ opt_batch_size = 2
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+ max_batch_size = 16
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+ if streaming:
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+ opt_batch_size, max_batch_size = 1, 1 # only support batch size 1 for streaming tts
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+ trt_kwargs = self.get_trt_kwargs_dynamic_batch(opt_batch_size=opt_batch_size, max_batch_size=max_batch_size, streaming=streaming)
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+ convert_onnx_to_trt(flow_decoder_estimator_model, trt_kwargs, flow_decoder_onnx_model, dtype)
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+ del self.flow.decoder.estimator
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+ import tensorrt as trt
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+ with open(flow_decoder_estimator_model, 'rb') as f:
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+ estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
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+ assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model)
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+ self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device)
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+
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+ def get_trt_kwargs_dynamic_batch(self, opt_batch_size=2, max_batch_size=64, streaming=False):
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+ if streaming:
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+ min_shape = [(2, 80, 4), (2, 80, 4), (2, 80, 4), (2,), (2, 80), (16, 2, 1024, 2), (16, 2, 8, 0, 128)]
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+ opt_shape = [
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+ (opt_batch_size * 2, 80, 500), (opt_batch_size * 2, 80, 500), (opt_batch_size * 2, 80, 500),
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+ (opt_batch_size * 2,), (opt_batch_size * 2, 80), (16, opt_batch_size * 2, 1024, 2),
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+ (16, opt_batch_size * 2, 8, 100, 128)
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+ ]
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+ max_shape = [
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+ (max_batch_size * 2, 80, 3000), (max_batch_size * 2, 80, 3000), (max_batch_size * 2, 80, 3000),
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+ (max_batch_size * 2,), (max_batch_size * 2, 80), (16, max_batch_size * 2, 1024, 2),
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+ (16, max_batch_size * 2, 8, 1000, 128)
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+ ]
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+ input_names = ["x", "mu", "cond", "t", "spks", "cnn_cache", "att_cache"]
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+ else:
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+ min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4), (2,), (2, 80)]
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+ opt_shape = [
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+ (opt_batch_size * 2, 80, 500), (opt_batch_size * 2, 1, 500), (opt_batch_size * 2, 80, 500),
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+ (opt_batch_size * 2, 80, 500), (opt_batch_size * 2,), (opt_batch_size * 2, 80)
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+ ]
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+ max_shape = [
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+ (max_batch_size * 2, 80, 3000), (max_batch_size * 2, 1, 3000), (max_batch_size * 2, 80, 3000),
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+ (max_batch_size * 2, 80, 3000), (max_batch_size * 2,), (max_batch_size * 2, 80)
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+ ]
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+ input_names = ["x", "mask", "mu", "cond", "t", "spks"]
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+ return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
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+
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+ def prompt_audio_tokenization(self, prompt_audios_list: list[torch.Tensor]) -> list[list[int]]:
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+ prompt_speech_tokens_list, prompt_speech_mels_list = [], []
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+ for audio in prompt_audios_list:
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+ assert len(audio.shape) == 1
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+ log_mel = s3tokenizer.log_mel_spectrogram(audio) # [num_mels, T]
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+ prompt_speech_mels_list.append(log_mel)
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+ prompt_mels_for_llm, prompt_mels_lens_for_llm = s3tokenizer.padding(prompt_speech_mels_list)
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+ prompt_speech_tokens, prompt_speech_tokens_lens = self.audio_tokenizer.quantize(
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+ prompt_mels_for_llm.to(self.device), prompt_mels_lens_for_llm.to(self.device)
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+ )
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+ for i in range(len(prompt_speech_tokens)):
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+ speech_tokens_i = prompt_speech_tokens[i, :prompt_speech_tokens_lens[i].item()].tolist()
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+ prompt_speech_tokens_list.append(speech_tokens_i)
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+ return prompt_speech_tokens_list
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+
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+ def get_spk_emb(self, prompt_audios_list: list[torch.Tensor]) -> torch.Tensor:
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+ spk_emb_for_flow = []
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+ for audio in prompt_audios_list:
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+ assert len(audio.shape) == 1
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+ spk_feat = kaldi.fbank(audio.unsqueeze(0), num_mel_bins=80, dither=0, sample_frequency=16000)
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+ spk_feat = spk_feat - spk_feat.mean(dim=0, keepdim=True)
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+ spk_emb = self.forward_spk_embedding(spk_feat)
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+
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+ spk_emb_for_flow.append(spk_emb)
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+ spk_emb_for_flow = torch.tensor(spk_emb_for_flow)
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+ if self.dtype != torch.float32:
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+ spk_emb_for_flow = spk_emb_for_flow.to(self.dtype)
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+ return spk_emb_for_flow
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+
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+ def get_prompt_mels(self, prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]):
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+ prompt_mels_for_flow = []
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+ prompt_mels_lens_for_flow = []
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+ for audio, sample_rate in zip(prompt_audios_list, prompt_audios_sample_rate):
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+ assert len(audio.shape) == 1
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+ audio = audio.unsqueeze(0)
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+ if sample_rate != 24000:
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+ audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=24000)(audio)
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+ mel = mel_spectrogram(audio).transpose(1, 2).squeeze(0) # [T, num_mels]
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+ mel_len = mel.shape[0]
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+ prompt_mels_for_flow.append(mel)
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+ prompt_mels_lens_for_flow.append(mel_len)
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+ prompt_mels_for_flow = torch.nn.utils.rnn.pad_sequence(
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+ prompt_mels_for_flow, batch_first=True, padding_value=0
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+ ) # [B, T', num_mels=80]
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+ prompt_mels_lens_for_flow = torch.tensor(prompt_mels_lens_for_flow)
|
|
|
+ return prompt_mels_for_flow, prompt_mels_lens_for_flow
|
|
|
+
|
|
|
+ def forward_flow(self, prompt_speech_tokens_list: list[list[int]],
|
|
|
+ generated_speech_tokens_list: list[list[int]],
|
|
|
+ prompt_mels_for_flow: torch.Tensor,
|
|
|
+ prompt_mels_lens_for_flow: torch.Tensor,
|
|
|
+ spk_emb_for_flow: torch.Tensor):
|
|
|
+ batch_size = prompt_mels_for_flow.shape[0]
|
|
|
+ flow_inputs = []
|
|
|
+ flow_inputs_lens = []
|
|
|
+ for prompt_speech_tokens, generated_speech_tokens in zip(prompt_speech_tokens_list, generated_speech_tokens_list):
|
|
|
+ flow_inputs.append(torch.tensor(prompt_speech_tokens + generated_speech_tokens))
|
|
|
+ flow_inputs_lens.append(len(prompt_speech_tokens) + len(generated_speech_tokens))
|
|
|
+
|
|
|
+ flow_inputs = torch.nn.utils.rnn.pad_sequence(flow_inputs, batch_first=True, padding_value=0)
|
|
|
+ flow_inputs_lens = torch.tensor(flow_inputs_lens)
|
|
|
+
|
|
|
+ with torch.amp.autocast(self.device, dtype=torch.float16):
|
|
|
+ generated_mels, generated_mels_lens = self.flow.inference(
|
|
|
+ flow_inputs.to(self.device), flow_inputs_lens.to(self.device),
|
|
|
+ prompt_mels_for_flow.to(self.device), prompt_mels_lens_for_flow.to(self.device), spk_emb_for_flow.to(self.device), 10
|
|
|
+ )
|
|
|
+
|
|
|
+ return generated_mels, generated_mels_lens
|
|
|
+
|
|
|
+ def forward_hift(self, generated_mels: torch.Tensor, generated_mels_lens: torch.Tensor, prompt_mels_lens_for_flow: torch.Tensor):
|
|
|
+ batch_size = generated_mels.shape[0]
|
|
|
+ generated_wavs = []
|
|
|
+ for i in range(batch_size):
|
|
|
+ mel = generated_mels[i, :, prompt_mels_lens_for_flow[i].item():generated_mels_lens[i].item()].unsqueeze(0)
|
|
|
+ wav, _ = self.hift(speech_feat=mel)
|
|
|
+ generated_wavs.append(wav)
|
|
|
+ return generated_wavs
|
|
|
+
|
|
|
+ @torch.inference_mode()
|
|
|
+ def forward(
|
|
|
+ self, generated_speech_tokens_list: list[list[int]], prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]
|
|
|
+ ):
|
|
|
+ assert all(sample_rate == 16000 for sample_rate in prompt_audios_sample_rate)
|
|
|
+
|
|
|
+ prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow = self.prepare_prompt_audio(prompt_audios_list, prompt_audios_sample_rate)
|
|
|
+
|
|
|
+ generated_mels, generated_mels_lens = self.forward_flow(
|
|
|
+ prompt_speech_tokens_list, generated_speech_tokens_list,
|
|
|
+ prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow
|
|
|
+ )
|
|
|
+
|
|
|
+ generated_wavs = self.forward_hift(generated_mels, generated_mels_lens, prompt_mels_lens_for_flow)
|
|
|
+ return generated_wavs
|
|
|
+
|
|
|
+ def prepare_prompt_audio(
|
|
|
+ self, prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]
|
|
|
+ ):
|
|
|
+ assert all(sample_rate == 16000 for sample_rate in prompt_audios_sample_rate)
|
|
|
+
|
|
|
+ prompt_speech_tokens_list = self.prompt_audio_tokenization(prompt_audios_list)
|
|
|
+
|
|
|
+ prompt_mels_for_flow, prompt_mels_lens_for_flow = self.get_prompt_mels(prompt_audios_list, prompt_audios_sample_rate)
|
|
|
+
|
|
|
+ spk_emb_for_flow = self.get_spk_emb(prompt_audios_list)
|
|
|
+ return prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow
|
|
|
+
|
|
|
+ def get_prompt_audio_cache_for_streaming_tts(
|
|
|
+ self, prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow
|
|
|
+ ):
|
|
|
+ assert len(prompt_speech_tokens_list) == 1, "only support batch size 1 for streaming tts"
|
|
|
+ for i, prompt_speech_tokens in enumerate(prompt_speech_tokens_list):
|
|
|
+ prompt_speech_tokens_list[i] = torch.tensor(prompt_speech_tokens + prompt_speech_tokens_list[i][:3])
|
|
|
+ prompt_speech_tokens_tensor = torch.nn.utils.rnn.pad_sequence(prompt_speech_tokens_list, batch_first=True, padding_value=0)
|
|
|
+
|
|
|
+ cache = self.flow.setup_cache(
|
|
|
+ prompt_speech_tokens_tensor.to(self.device),
|
|
|
+ prompt_mels_for_flow.to(self.device),
|
|
|
+ spk_emb_for_flow.to(self.device),
|
|
|
+ n_timesteps=10
|
|
|
+ )
|
|
|
+ new_cache = {k: v.clone() for k, v in cache.items()}
|
|
|
+ # Hack: this is a hack to avoid in-place changes to the cache['estimator_att_cache'] and cache['estimator_cnn_cache']
|
|
|
+ return new_cache
|
|
|
+
|
|
|
+ @torch.inference_mode()
|
|
|
+ def forward_streaming(
|
|
|
+ self, generated_speech_tokens: list[int], last_chunk: bool, request_id: str, speaker_id: str, prompt_audio: torch.Tensor = None, prompt_audio_sample_rate: int = 16000
|
|
|
+ ):
|
|
|
+ if speaker_id not in self.speaker_cache:
|
|
|
+ assert prompt_audio is not None, "prompt_audio is required for new speaker"
|
|
|
+ assert prompt_audio_sample_rate == 16000
|
|
|
+
|
|
|
+ prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow = self.prepare_prompt_audio([prompt_audio], [prompt_audio_sample_rate])
|
|
|
+
|
|
|
+ token_len = min(int(prompt_mels_for_flow.shape[1] / 2), len(prompt_speech_tokens_list[0]))
|
|
|
+ prompt_mels_for_flow = prompt_mels_for_flow[:, :2 * token_len].contiguous()
|
|
|
+ prompt_speech_tokens_list[0] = prompt_speech_tokens_list[0][:token_len]
|
|
|
+
|
|
|
+ prompt_audio_dict = {'spk_emb_for_flow': spk_emb_for_flow, 'prompt_mels_for_flow': prompt_mels_for_flow}
|
|
|
+
|
|
|
+ cache_dict = self.get_prompt_audio_cache_for_streaming_tts(prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow)
|
|
|
+ self.speaker_cache[speaker_id] = {'prompt_audio_dict': prompt_audio_dict, 'cache_dict': cache_dict}
|
|
|
+
|
|
|
+ if request_id not in self.streaming_flow_cache:
|
|
|
+ self.streaming_flow_cache[request_id] = {k: v.clone() for k, v in self.speaker_cache[speaker_id]['cache_dict'].items()}
|
|
|
+ self.hift_cache_dict[request_id] = dict(
|
|
|
+ mel=torch.zeros(1, 80, 0, device='cuda'),
|
|
|
+ source=torch.zeros(1, 1, 0, device='cuda'),
|
|
|
+ speech=torch.zeros(1, 0, device='cuda'),
|
|
|
+ )
|
|
|
+
|
|
|
+ current_request_cache = self.streaming_flow_cache[request_id]
|
|
|
+
|
|
|
+ current_prompt_audio_dict = self.speaker_cache[speaker_id]['prompt_audio_dict']
|
|
|
+ generated_speech_tokens = torch.tensor([generated_speech_tokens], dtype=torch.int32, device='cuda')
|
|
|
+
|
|
|
+ chunk_mel, new_streaming_flow_cache = self.flow.inference_chunk(
|
|
|
+ token=generated_speech_tokens,
|
|
|
+ spk=current_prompt_audio_dict['spk_emb_for_flow'].to(self.device),
|
|
|
+ cache=current_request_cache,
|
|
|
+ last_chunk=last_chunk,
|
|
|
+ n_timesteps=10,
|
|
|
+ )
|
|
|
+
|
|
|
+ self.streaming_flow_cache[request_id] = new_streaming_flow_cache
|
|
|
+
|
|
|
+ if self.streaming_flow_cache[request_id]['estimator_att_cache'].shape[4] > (current_prompt_audio_dict['prompt_mels_for_flow'].shape[1] + 100):
|
|
|
+ self.streaming_flow_cache[request_id]['estimator_att_cache'] = torch.cat([
|
|
|
+ self.streaming_flow_cache[request_id]['estimator_att_cache'][:, :, :, :, :current_prompt_audio_dict['prompt_mels_for_flow'].shape[1]],
|
|
|
+ self.streaming_flow_cache[request_id]['estimator_att_cache'][:, :, :, :, -100:],
|
|
|
+ ], dim=4)
|
|
|
+
|
|
|
+ hift_cache_mel = self.hift_cache_dict[request_id]['mel'].clone()
|
|
|
+ hift_cache_source = self.hift_cache_dict[request_id]['source'].clone()
|
|
|
+ hift_cache_speech = self.hift_cache_dict[request_id]['speech'].clone()
|
|
|
+ mel = torch.concat([hift_cache_mel, chunk_mel], dim=2).clone()
|
|
|
+
|
|
|
+ speech, source = self.hift(mel, hift_cache_source)
|
|
|
+
|
|
|
+ # overlap speech smooth
|
|
|
+ if hift_cache_speech.shape[-1] > 0:
|
|
|
+ speech = fade_in_out(speech, hift_cache_speech, self.speech_window)
|
|
|
+
|
|
|
+ # update vocoder cache
|
|
|
+ self.hift_cache_dict[request_id] = dict(
|
|
|
+ mel=mel[..., -self.mel_cache_len:].clone().detach(),
|
|
|
+ source=source[:, :, -self.source_cache_len:].clone().detach(),
|
|
|
+ speech=speech[:, -self.source_cache_len:].clone().detach(),
|
|
|
+ )
|
|
|
+ if not last_chunk:
|
|
|
+ speech = speech[:, :-self.source_cache_len]
|
|
|
+
|
|
|
+ if last_chunk:
|
|
|
+ assert request_id in self.streaming_flow_cache
|
|
|
+ self.streaming_flow_cache.pop(request_id)
|
|
|
+ self.hift_cache_dict.pop(request_id)
|
|
|
+
|
|
|
+ return speech
|
|
|
+
|
|
|
+
|
|
|
+def collate_fn(batch):
|
|
|
+ ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = [], [], [], []
|
|
|
+ for item in batch:
|
|
|
+ generated_speech_tokens_list.append(item['target_audio_cosy2_tokens'])
|
|
|
+ audio = torch.from_numpy(item['prompt_audio']['array']).float()
|
|
|
+ prompt_audios_list.append(audio)
|
|
|
+ prompt_audios_sample_rate.append(item['prompt_audio']['sampling_rate'])
|
|
|
+ ids.append(item['id'])
|
|
|
+
|
|
|
+ return ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate
|
|
|
+
|
|
|
+
|
|
|
+def get_args():
|
|
|
+ parser = argparse.ArgumentParser()
|
|
|
+ parser.add_argument("--enable-trt", action="store_true")
|
|
|
+ parser.add_argument("--model-dir", type=str, default="./Step-Audio-2-mini/token2wav")
|
|
|
+ parser.add_argument("--batch-size", type=int, default=1)
|
|
|
+ parser.add_argument("--output-dir", type=str, default="generated_wavs")
|
|
|
+ parser.add_argument("--huggingface-dataset-split", type=str, default="wenetspeech4tts")
|
|
|
+ parser.add_argument("--warmup", type=int, default=3, help="Number of warmup epochs, performance statistics will only be collected from the last epoch")
|
|
|
+ return parser.parse_args()
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ args = get_args()
|
|
|
+ model = CosyVoice2_Token2Wav(model_dir=args.model_dir, enable_trt=args.enable_trt)
|
|
|
+ if not os.path.exists(args.output_dir):
|
|
|
+ os.makedirs(args.output_dir)
|
|
|
+ dataset_name = "yuekai/seed_tts_cosy2"
|
|
|
+
|
|
|
+ dataset = load_dataset(dataset_name, split=args.huggingface_dataset_split, trust_remote_code=True)
|
|
|
+
|
|
|
+ data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn, num_workers=0)
|
|
|
+
|
|
|
+ for _ in range(args.warmup):
|
|
|
+ start_time = time.time()
|
|
|
+ for batch in data_loader:
|
|
|
+ ids, generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate = batch
|
|
|
+
|
|
|
+ generated_wavs = model(generated_speech_tokens_list, prompt_audios_list, prompt_audios_sample_rate)
|
|
|
+
|
|
|
+ for id, wav in zip(ids, generated_wavs):
|
|
|
+ torchaudio.save(f"{args.output_dir}/{id}.wav", wav.cpu(), 24000)
|
|
|
+ end_time = time.time()
|
|
|
+ epoch_time = end_time - start_time
|
|
|
+ print(f"Measurement epoch time taken: {epoch_time:.4f} seconds")
|
Xiang Lyu