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@ -153,7 +153,7 @@ class T2SEngine(T2SEngineProtocol):
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pos_sorted = mx.sort(pos, axis=0)
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valid_count = session.bsz - mx.sum(cast(Array, pos_sorted == session.bsz))
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pos_final = pos_sorted[: int(valid_count)]
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newly_done_indices = mx.expand_dims(newly_done_indices[pos_final], 0)
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newly_done_indices = newly_done_indices[pos_final]
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mx.set_default_device(self.device)
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if debug:
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@ -4,6 +4,7 @@ import os
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import random
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import time
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import traceback
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import warnings
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from copy import deepcopy
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from pathlib import Path
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from typing import Any
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@ -19,7 +20,7 @@ from peft import LoraConfig, get_peft_model
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from tqdm import tqdm
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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from GPT_SoVITS.Accelerate import MLX, PyTorch, T2SEngineProtocol, T2SRequest, backends
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from GPT_SoVITS.Accelerate import MLX, PyTorch, T2SEngineProtocol, T2SRequest, backends, console
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from GPT_SoVITS.BigVGAN.bigvgan import BigVGAN
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from GPT_SoVITS.feature_extractor.cnhubert import CNHubert
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from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch, spectrogram_torch
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@ -37,6 +38,14 @@ now_dir = os.getcwd()
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resample_transform_dict = {}
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v3v4set = {"v3", "v4"}
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warnings.filterwarnings(
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"ignore", message="MPS: The constant padding of more than 3 dimensions is not currently supported natively."
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)
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warnings.filterwarnings("ignore", message=".*ComplexHalf support is experimental.*")
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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def resample(audio_tensor, sr0, sr1, device):
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global resample_transform_dict
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@ -963,6 +972,7 @@ class TTS:
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Tuple[int, np.ndarray]: sampling rate and audio data.
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"""
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########## variables initialization ###########
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ttfb_time = time.perf_counter()
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self.stop_flag: bool = False
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text: str = inputs.get("text", "")
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text_lang: str = inputs.get("text_lang", "")
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@ -1013,6 +1023,7 @@ class TTS:
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)
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###### setting reference audio and prompt text preprocessing ########
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t_34 = t_45 = 0.0
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t0 = time.perf_counter()
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if (ref_audio_path is not None) and (
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ref_audio_path != self.prompt_cache["ref_audio_path"]
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@ -1107,6 +1118,9 @@ class TTS:
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return batch[0]
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t2 = time.perf_counter()
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infer_len: list[int] = []
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infer_time: list[float] = []
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audio_len = [0.0]
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try:
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print("############ 推理 ############")
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###### inference ######
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@ -1114,7 +1128,7 @@ class TTS:
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t_45 = 0.0
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audio = []
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output_sr = self.configs.sampling_rate if not self.configs.use_vocoder else self.vocoder_configs["sr"]
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for item in data:
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for idx, item in enumerate(data):
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t3 = time.perf_counter()
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if return_fragment:
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item = make_batch(item)
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@ -1158,6 +1172,8 @@ class TTS:
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pred_semantic_list = t2s_result.result
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assert pred_semantic_list
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pred_semantic_list = [semantic.squeeze(0) for semantic in pred_semantic_list]
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infer_len.append(t2s_result.total_tokens)
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infer_time.append(t2s_result.infer_speed[-1])
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t4 = time.perf_counter()
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t_34 += t4 - t3
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@ -1246,12 +1262,13 @@ class TTS:
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_pred_semantic, phones, speed=speed_factor, sample_steps=sample_steps
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)
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batch_audio_fragment.append(audio_fragment)
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if idx == 0:
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ttfb_time = time.perf_counter() - ttfb_time
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t5 = time.perf_counter()
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t_45 += t5 - t4
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if return_fragment:
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
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yield self.audio_postprocess(
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console.print(f">> Time Stamps For Fragment: {t_34:.3f}\t{t_45:.3f}")
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tmp = self.audio_postprocess(
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[batch_audio_fragment],
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output_sr,
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None,
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@ -1260,19 +1277,23 @@ class TTS:
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fragment_interval,
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super_sampling if self.configs.use_vocoder and self.configs.version == "v3" else False,
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)
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audio_len.append(len(tmp[-1]) / output_sr)
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yield tmp
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else:
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audio.append(batch_audio_fragment)
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if self.stop_flag:
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audio_len.append(1)
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yield 16000, np.zeros(int(16000), dtype=np.int16)
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return
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if not return_fragment:
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
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if len(audio) == 0:
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audio_len.append(1)
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yield 16000, np.zeros(int(16000), dtype=np.int16)
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return
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yield self.audio_postprocess(
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tmp = self.audio_postprocess(
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audio,
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output_sr,
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batch_index_list,
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@ -1281,10 +1302,14 @@ class TTS:
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fragment_interval,
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super_sampling if self.configs.use_vocoder and self.configs.version == "v3" else False,
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)
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audio_len.append(len(tmp[-1]) / output_sr)
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yield tmp
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except Exception as e:
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traceback.print_exc()
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# 必须返回一个空音频, 否则会导致显存不释放。
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audio_len.append(1)
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yield 16000, np.zeros(int(16000), dtype=np.int16)
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# 重置模型, 否则会导致显存释放不完全。
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del self.t2s_model
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@ -1295,6 +1320,16 @@ class TTS:
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self.init_vits_weights(self.configs.vits_weights_path)
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raise e
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finally:
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infer_speed_avg = sum(infer_len) / sum(infer_time)
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rtf_value = sum((t1 - t0, t2 - t1, t_34, t_45)) / sum(audio_len)
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console.print(f">> Time Stamps: {t1 - t0:.3f}\t{t2 - t1:.3f}\t{t_34:.3f}\t{t_45:.3f}")
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console.print(f">> Infer Speed: {infer_speed_avg:.2f} Token/s")
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console.print(f">> RTF: {rtf_value:.2f}")
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if ttfb_time > 2:
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console.print(f">> TTFB: {ttfb_time:.3f} s")
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else:
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console.print(f">> TTFB: {ttfb_time * 1000:.3f} ms")
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self.empty_cache()
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def empty_cache(self):
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