# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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. """ Example Usage CUDA_VISIBLE_DEVICES=0 \ python3 token2wav.py --enable-trt || exit 1 """ import torch # from flashcosyvoice.modules.flow import CausalMaskedDiffWithXvec from flashcosyvoice.modules.hifigan import HiFTGenerator from flashcosyvoice.utils.audio import mel_spectrogram import torchaudio.compliance.kaldi as kaldi import onnxruntime import s3tokenizer from torch.utils.data import DataLoader from datasets import load_dataset import torchaudio import os import logging import argparse import queue import time import numpy as np from hyperpyyaml import load_hyperpyyaml def fade_in_out(fade_in_mel:torch.Tensor, fade_out_mel:torch.Tensor, window:torch.Tensor): """perform fade_in_out in tensor style """ mel_overlap_len = int(window.shape[0] / 2) fade_in_mel = fade_in_mel.clone() fade_in_mel[..., :mel_overlap_len] = \ fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \ fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:] return fade_in_mel def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, dtype): import tensorrt as trt logging.info("Converting onnx to trt...") network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) logger = trt.Logger(trt.Logger.INFO) builder = trt.Builder(logger) network = builder.create_network(network_flags) parser = trt.OnnxParser(network, logger) config = builder.create_builder_config() # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 32) # 4GB if dtype == torch.float16: config.set_flag(trt.BuilderFlag.FP16) elif dtype == torch.bfloat16: config.set_flag(trt.BuilderFlag.BF16) elif dtype == torch.float32: config.set_flag(trt.BuilderFlag.FP32) profile = builder.create_optimization_profile() # load onnx model with open(onnx_model, "rb") as f: if not parser.parse(f.read()): for error in range(parser.num_errors): print(parser.get_error(error)) raise ValueError('failed to parse {}'.format(onnx_model)) # set input shapes for i in range(len(trt_kwargs['input_names'])): profile.set_shape(trt_kwargs['input_names'][i], trt_kwargs['min_shape'][i], trt_kwargs['opt_shape'][i], trt_kwargs['max_shape'][i]) if dtype == torch.float16: tensor_dtype = trt.DataType.HALF elif dtype == torch.bfloat16: tensor_dtype = trt.DataType.BF16 elif dtype == torch.float32: tensor_dtype = trt.DataType.FLOAT else: raise ValueError('invalid dtype {}'.format(dtype)) # set input and output data type for i in range(network.num_inputs): input_tensor = network.get_input(i) input_tensor.dtype = tensor_dtype for i in range(network.num_outputs): output_tensor = network.get_output(i) output_tensor.dtype = tensor_dtype config.add_optimization_profile(profile) engine_bytes = builder.build_serialized_network(network, config) # save trt engine with open(trt_model, "wb") as f: f.write(engine_bytes) logging.info("Succesfully convert onnx to trt...") class TrtContextWrapper: def __init__(self, trt_engine, trt_concurrent=1, device='cuda:0'): self.trt_context_pool = queue.Queue(maxsize=trt_concurrent) self.trt_engine = trt_engine self.device = device for _ in range(trt_concurrent): trt_context = trt_engine.create_execution_context() trt_stream = torch.cuda.stream(torch.cuda.Stream(torch.device(device))) assert trt_context is not None, 'failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}'.format(trt_concurrent) self.trt_context_pool.put([trt_context, trt_stream]) assert self.trt_context_pool.empty() is False, 'no avaialbe estimator context' def acquire_estimator(self): return self.trt_context_pool.get(), self.trt_engine def release_estimator(self, context, stream): self.trt_context_pool.put([context, stream]) class CosyVoice2_Token2Wav(torch.nn.Module): def __init__(self, model_dir: str, enable_trt: bool = False, device_id: int = 0, streaming: bool = False, dtype: torch.dtype = torch.float16): super().__init__() self.device_id = device_id self.device = f"cuda:{device_id}" with open(f"{model_dir}/flow.yaml", "r") as f: configs = load_hyperpyyaml(f) self.flow = configs['flow'] self.dtype = dtype self.flow.to(self.dtype) self.flow.load_state_dict(torch.load(f"{model_dir}/flow.pt", map_location="cpu", weights_only=True), strict=True) self.flow.to(self.device).eval() self.hift = HiFTGenerator() 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()} self.hift.load_state_dict(hift_state_dict, strict=True) self.hift.to(self.device).eval() option = onnxruntime.SessionOptions() option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL option.intra_op_num_threads = 1 self.spk_model = onnxruntime.InferenceSession(f"{model_dir}/campplus.onnx", sess_options=option, providers=["CPUExecutionProvider"]) self.audio_tokenizer = s3tokenizer.load_model(f"{model_dir}/speech_tokenizer_v2_25hz.onnx").to(self.device).eval() gpu="l20" if enable_trt: if streaming: self.load_trt(f'{model_dir}/flow.decoder.estimator.{self.dtype}.dynamic_batch.chunk.{gpu}.plan', f'{model_dir}/flow.decoder.estimator.chunk.fp32.dynamic_batch.simplify.onnx', 1, self.dtype, streaming) else: self.load_trt(f'{model_dir}/flow.decoder.estimator.{self.dtype}.dynamic_batch.{gpu}.plan', f'{model_dir}/flow.decoder.estimator.fp32.dynamic_batch.onnx', 1, self.dtype) self.load_spk_trt(f'{model_dir}/campplus.{gpu}.fp32.trt', f'{model_dir}/campplus.onnx', 1, False) self.streaming_flow_cache = {} self.speaker_cache = {} self.mel_cache_len = 8 # hard-coded, 160ms self.source_cache_len = int(self.mel_cache_len * 480) # 50hz mel -> 24kHz wave self.speech_window = torch.from_numpy(np.hamming(2 * self.source_cache_len)).cuda() # hifigan cache for streaming tts self.hift_cache_dict = {} def forward_spk_embedding(self, spk_feat): if isinstance(self.spk_model, onnxruntime.InferenceSession): return self.spk_model.run( None, {self.spk_model.get_inputs()[0].name: spk_feat.unsqueeze(dim=0).cpu().numpy()} )[0].flatten().tolist() else: [spk_model, stream], trt_engine = self.spk_model.acquire_estimator() # NOTE need to synchronize when switching stream with torch.cuda.device(self.device_id): torch.cuda.current_stream().synchronize() spk_feat = spk_feat.unsqueeze(dim=0).to(self.device) batch_size = spk_feat.size(0) with stream: spk_model.set_input_shape('input', (batch_size, spk_feat.size(1), 80)) output_tensor = torch.empty((batch_size, 192), device=spk_feat.device) data_ptrs = [spk_feat.contiguous().data_ptr(), output_tensor.contiguous().data_ptr()] for i, j in enumerate(data_ptrs): spk_model.set_tensor_address(trt_engine.get_tensor_name(i), j) # run trt engine assert spk_model.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True torch.cuda.current_stream().synchronize() self.spk_model.release_estimator(spk_model, stream) return output_tensor.cpu().numpy().flatten().tolist() def load_spk_trt(self, spk_model, spk_onnx_model, trt_concurrent=1, fp16=True): if not os.path.exists(spk_model) or os.path.getsize(spk_model) == 0: trt_kwargs = self.get_spk_trt_kwargs() convert_onnx_to_trt(spk_model, trt_kwargs, spk_onnx_model, fp16) import tensorrt as trt with open(spk_model, 'rb') as f: spk_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) assert spk_engine is not None, 'failed to load trt {}'.format(spk_model) self.spk_model = TrtContextWrapper(spk_engine, trt_concurrent=trt_concurrent, device=self.device) def get_spk_trt_kwargs(self): min_shape = [(1, 4, 80)] opt_shape = [(1, 500, 80)] max_shape = [(1, 3000, 80)] input_names = ["input"] return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent=1, dtype=torch.float16, streaming=False): 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: opt_batch_size = 2 max_batch_size = 16 if streaming: opt_batch_size, max_batch_size = 1, 1 # only support batch size 1 for streaming tts trt_kwargs = self.get_trt_kwargs_dynamic_batch(opt_batch_size=opt_batch_size, max_batch_size=max_batch_size, streaming=streaming) convert_onnx_to_trt(flow_decoder_estimator_model, trt_kwargs, flow_decoder_onnx_model, dtype) 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_dynamic_batch(self, opt_batch_size=2, max_batch_size=64, streaming=False): if streaming: min_shape = [(2, 80, 4), (2, 80, 4), (2, 80, 4), (2,), (2, 80), (16, 2, 1024, 2), (16, 2, 8, 0, 128)] opt_shape = [(opt_batch_size*2, 80, 500), (opt_batch_size*2, 80, 500), (opt_batch_size*2, 80, 500), (opt_batch_size*2,), (opt_batch_size*2, 80), (16, opt_batch_size*2, 1024, 2), (16, opt_batch_size*2, 8, 100, 128)] max_shape = [(max_batch_size*2, 80, 3000), (max_batch_size*2, 80, 3000), (max_batch_size*2, 80, 3000), (max_batch_size*2,), (max_batch_size*2, 80), (16, max_batch_size*2, 1024, 2), (16, max_batch_size*2, 8, 1000, 128)] input_names = ["x", "mu", "cond", "t", "spks", "cnn_cache", "att_cache"] else: min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4), (2,), (2, 80)] opt_shape = [(opt_batch_size*2, 80, 500), (opt_batch_size*2, 1, 500), (opt_batch_size*2, 80, 500), (opt_batch_size*2, 80, 500), (opt_batch_size*2,), (opt_batch_size*2, 80)] max_shape = [(max_batch_size*2, 80, 3000), (max_batch_size*2, 1, 3000), (max_batch_size*2, 80, 3000), (max_batch_size*2, 80, 3000), (max_batch_size*2,), (max_batch_size*2, 80)] input_names = ["x", "mask", "mu", "cond", "t", "spks"] return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} def prompt_audio_tokenization(self, prompt_audios_list: list[torch.Tensor]) -> list[list[int]]: prompt_speech_tokens_list, prompt_speech_mels_list = [], [] for audio in prompt_audios_list: assert len(audio.shape) == 1 log_mel = s3tokenizer.log_mel_spectrogram(audio) # [num_mels, T] prompt_speech_mels_list.append(log_mel) prompt_mels_for_llm, prompt_mels_lens_for_llm = s3tokenizer.padding(prompt_speech_mels_list) prompt_speech_tokens, prompt_speech_tokens_lens = self.audio_tokenizer.quantize( prompt_mels_for_llm.to(self.device), prompt_mels_lens_for_llm.to(self.device) ) for i in range(len(prompt_speech_tokens)): speech_tokens_i = prompt_speech_tokens[i, :prompt_speech_tokens_lens[i].item()].tolist() prompt_speech_tokens_list.append(speech_tokens_i) return prompt_speech_tokens_list def get_spk_emb(self, prompt_audios_list: list[torch.Tensor]) -> torch.Tensor: spk_emb_for_flow = [] for audio in prompt_audios_list: assert len(audio.shape) == 1 spk_feat = kaldi.fbank(audio.unsqueeze(0), num_mel_bins=80, dither=0, sample_frequency=16000) spk_feat = spk_feat - spk_feat.mean(dim=0, keepdim=True) spk_emb = self.forward_spk_embedding(spk_feat) spk_emb_for_flow.append(spk_emb) spk_emb_for_flow = torch.tensor(spk_emb_for_flow) if self.dtype != torch.float32: spk_emb_for_flow = spk_emb_for_flow.to(self.dtype) return spk_emb_for_flow def get_prompt_mels(self, prompt_audios_list: list[torch.Tensor], prompt_audios_sample_rate: list[int]): prompt_mels_for_flow = [] prompt_mels_lens_for_flow = [] for audio, sample_rate in zip(prompt_audios_list, prompt_audios_sample_rate): assert len(audio.shape) == 1 audio = audio.unsqueeze(0) if sample_rate != 24000: audio = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=24000)(audio) mel = mel_spectrogram(audio).transpose(1, 2).squeeze(0) # [T, num_mels] mel_len = mel.shape[0] prompt_mels_for_flow.append(mel) prompt_mels_lens_for_flow.append(mel_len) prompt_mels_for_flow = torch.nn.utils.rnn.pad_sequence(prompt_mels_for_flow, batch_first=True, padding_value=0) # [B, T', num_mels=80] 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 item in prompt_audios_sample_rate is 16000 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 item in prompt_audios_sample_rate is 16000 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} print(f"speaker_id {speaker_id} added to cache") 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'), ) # else: # for k, v in self.streaming_flow_cache[request_id].items(): # print(f"k: {k}, v: {v.shape}, dtype: {v.dtype}") # for k, v in self.hift_cache_dict[request_id].items(): # print(f"k: {k}, v: {v.shape}, dtype: {v.dtype}") # breakpoint() 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 i, item in enumerate(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) # mkdir output_dir if not exists 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 epoch 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")