Lucy-TTS/F5: Skripte + Batches versionieren, schwere Assets ignoriert
- pocket_server.py (Produktions-TTS mit Stimmen-Waechter), text_norm, Bench-/Diag-Skripte - lucy-f5: f5_server/f5_test/bench_dml (DirectML-Experiment, Phase C/D offen) - .gitignore: venvs/Modelle/Audio/Logs der beiden Ordner + box_recon/gemma_swap-Scratch Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Hitonabi
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import re
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import site
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import time
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import jieba
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import torch
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import onnxruntime
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import soundfile as sf
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import numpy as np
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from pydub import AudioSegment
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from pypinyin import lazy_pinyin, Style
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python_package_path = site.getsitepackages()[-1]
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vocab_path = "/home/DakeQQ/Downloads/F5TTS_v1_Base/vocab.txt" # The F5-TTS model vocab download path. URL: https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_v1_Base
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onnx_model_A = "/home/DakeQQ/Downloads/F5_Optimized/F5_Preprocess.onnx" # The exported onnx model path.
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onnx_model_B = "/home/DakeQQ/Downloads/F5_Optimized/F5_Transformer.onnx" # The exported onnx model path.
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onnx_model_C = "/home/DakeQQ/Downloads/F5_Optimized/F5_Decode.onnx" # The exported onnx model path.
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generated_audio = "./generated_audio.wav"
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test_in_english = False
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if test_in_english:
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reference_audio = python_package_path + "/f5_tts/infer/examples/basic/basic_ref_en.wav"
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ref_text = "Some call me nature, others call me mother nature."
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gen_text = "Some call me Dake, others call me QQ."
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else:
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reference_audio = python_package_path + "/f5_tts/infer/examples/basic/basic_ref_zh.wav" # The reference audio path.
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ref_text = "对，这就是我，万人敬仰的太乙真人。" # The ASR result of reference audio.
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gen_text = "对，这就是我，万人敬仰的大可奇奇。" # The target TTS.
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ORT_Accelerate_Providers = ['CPUExecutionProvider'] # If you have accelerate devices for : ['CUDAExecutionProvider', 'TensorrtExecutionProvider', 'CoreMLExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'ROCMExecutionProvider', 'MIGraphXExecutionProvider', 'AzureExecutionProvider']
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# else keep empty.
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RANDOM_SEED = 9527 # Set seed to reproduce the generated audio
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NFE_STEP = 32 # F5-TTS model setting, 0~31
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FUSE_NFE = 1 # Maintain the same values as the exported model.
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SPEED = 1.0 # Set for talking speed. Only works with dynamic_axes=True
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MAX_THREADS = 8 # Max CPU parallel threads.
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DEVICE_ID = 0 # The GPU id, default to 0.
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MODEL_SAMPLE_RATE = 24000 # Do not modify it.
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HOP_LENGTH = 256 # It affects the generated audio length and speech speed.
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if "OpenVINOExecutionProvider" in ORT_Accelerate_Providers:
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provider_options = [
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{
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'device_type': 'CPU', # [CPU, NPU, GPU, GPU.0, GPU.1]]
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'precision': 'ACCURACY', # [FP32, FP16, ACCURACY]
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'num_of_threads': MAX_THREADS,
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'num_streams': 1,
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'enable_opencl_throttling': True,
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'enable_qdq_optimizer': False # Enable it carefully
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}
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]
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elif "CUDAExecutionProvider" in ORT_Accelerate_Providers:
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provider_options = [
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{
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'device_id': DEVICE_ID,
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'gpu_mem_limit': 8 * 1024 * 1024 * 1024, # 8 GB
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'arena_extend_strategy': 'kNextPowerOfTwo',
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'cudnn_conv_algo_search': 'EXHAUSTIVE',
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'cudnn_conv_use_max_workspace': '1',
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'do_copy_in_default_stream': '1',
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'cudnn_conv1d_pad_to_nc1d': '1',
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'enable_cuda_graph': '0', # Set to '0' to avoid potential errors when enabled.
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'use_tf32': '0'
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}
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]
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else:
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# Please config by yourself for others providers.
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provider_options = None
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with open(vocab_path, "r", encoding="utf-8") as f:
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vocab_char_map = {}
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for i, char in enumerate(f):
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vocab_char_map[char[:-1]] = i
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vocab_size = len(vocab_char_map)
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# From the official code
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def convert_char_to_pinyin(text_list, polyphone=True):
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if jieba.dt.initialized is False:
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jieba.default_logger.setLevel(50) # CRITICAL
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jieba.initialize()
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final_text_list = []
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custom_trans = str.maketrans(
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{";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
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) # add custom trans here, to address oov
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def is_chinese(c):
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return (
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"\u3100" <= c <= "\u9fff" # common chinese characters
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)
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for text in text_list:
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char_list = []
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text = text.translate(custom_trans)
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for seg in jieba.cut(text):
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seg_byte_len = len(bytes(seg, "UTF-8"))
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if seg_byte_len == len(seg): # if pure alphabets and symbols
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if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
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char_list.append(" ")
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char_list.extend(seg)
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elif polyphone and seg_byte_len == 3 * len(seg): # if pure east asian characters
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seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
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for i, c in enumerate(seg):
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if is_chinese(c):
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char_list.append(" ")
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char_list.append(seg_[i])
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else: # if mixed characters, alphabets and symbols
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for c in seg:
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if ord(c) < 256:
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char_list.extend(c)
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elif is_chinese(c):
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char_list.append(" ")
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char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
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else:
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char_list.append(c)
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final_text_list.append(char_list)
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return final_text_list
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# From the official code
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def list_str_to_idx(
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text: list[str] | list[list[str]],
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vocab_char_map: dict[str, int], # {char: idx}
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padding_value=-1
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):
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get_idx = vocab_char_map.get
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list_idx_tensors = [torch.tensor([get_idx(c, 0) for c in t], dtype=torch.int32) for t in text]
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text = torch.nn.utils.rnn.pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
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return text
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def normalize_to_int16(audio):
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max_val = np.max(np.abs(audio))
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scaling_factor = 32767.0 / max_val if max_val > 0 else 1.0
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return (audio * float(scaling_factor)).astype(np.int16)
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# ONNX Runtime settings
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onnxruntime.set_seed(RANDOM_SEED)
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session_opts = onnxruntime.SessionOptions()
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session_opts.log_severity_level = 4 # fatal level = 4, it an adjustable value.
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session_opts.log_verbosity_level = 4 # fatal level = 4, it an adjustable value.
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session_opts.inter_op_num_threads = MAX_THREADS # Run different nodes with num_threads. Set 0 for auto.
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session_opts.intra_op_num_threads = MAX_THREADS # Under the node, execute the operators with num_threads. Set 0 for auto.
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session_opts.enable_cpu_mem_arena = True # True for execute speed; False for less memory usage.
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session_opts.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
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session_opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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session_opts.add_session_config_entry("session.intra_op.allow_spinning", "1")
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session_opts.add_session_config_entry("session.inter_op.allow_spinning", "1")
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session_opts.add_session_config_entry("session.set_denormal_as_zero", "1")
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session_opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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ort_session_A = onnxruntime.InferenceSession(onnx_model_A, sess_options=session_opts, providers=['CPUExecutionProvider'], provider_options=None)
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model_type = ort_session_A._inputs_meta[0].type
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in_name_A = ort_session_A.get_inputs()
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out_name_A = ort_session_A.get_outputs()
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in_name_A0 = in_name_A[0].name
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in_name_A1 = in_name_A[1].name
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in_name_A2 = in_name_A[2].name
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out_name_A0 = out_name_A[0].name
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out_name_A1 = out_name_A[1].name
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out_name_A2 = out_name_A[2].name
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out_name_A3 = out_name_A[3].name
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out_name_A4 = out_name_A[4].name
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out_name_A5 = out_name_A[5].name
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out_name_A6 = out_name_A[6].name
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out_name_A7 = out_name_A[7].name
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if "CPUExecutionProvider" in ORT_Accelerate_Providers or not ORT_Accelerate_Providers:
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session_opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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else:
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session_opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASIC
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ort_session_B = onnxruntime.InferenceSession(onnx_model_B, sess_options=session_opts, providers=ORT_Accelerate_Providers, provider_options=provider_options)
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ORT_Accelerate_Providers = ort_session_B.get_providers()[0]
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# For Windows DirectML + Intel/AMD/Nvidia GPU,
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# pip install onnxruntime-directml --upgrade
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# ort_session_B = onnxruntime.InferenceSession(onnx_model_B, sess_options=session_opts, providers=['DmlExecutionProvider'])
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print(f"\nUsable Providers: {ORT_Accelerate_Providers}")
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model_dtype = ort_session_B._inputs_meta[0].type
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in_name_B = ort_session_B.get_inputs()
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out_name_B = ort_session_B.get_outputs()
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in_name_B0 = in_name_B[0].name
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in_name_B1 = in_name_B[1].name
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in_name_B2 = in_name_B[2].name
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in_name_B3 = in_name_B[3].name
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in_name_B4 = in_name_B[4].name
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in_name_B5 = in_name_B[5].name
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in_name_B6 = in_name_B[6].name
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in_name_B7 = in_name_B[7].name
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out_name_B0 = out_name_B[0].name
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out_name_B1 = out_name_B[1].name
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session_opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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ort_session_C = onnxruntime.InferenceSession(onnx_model_C, sess_options=session_opts, providers=['CPUExecutionProvider'], provider_options=None)
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in_name_C = ort_session_C.get_inputs()
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out_name_C = ort_session_C.get_outputs()
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in_name_C0 = in_name_C[0].name
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in_name_C1 = in_name_C[1].name
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out_name_C0 = out_name_C[0].name
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# Load the input audio
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print(f"\nReference Audio: {reference_audio}")
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audio = np.array(AudioSegment.from_file(reference_audio).set_channels(1).set_frame_rate(MODEL_SAMPLE_RATE).get_array_of_samples(), dtype=np.float32)
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audio = normalize_to_int16(audio)
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audio_len = len(audio)
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audio = audio.reshape(1, 1, -1)
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zh_pause_punc = r"。，、；：？！"
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ref_text_len = len(ref_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, ref_text))
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gen_text_len = len(gen_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, gen_text))
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ref_audio_len = audio_len // HOP_LENGTH + 1
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max_duration = np.array([ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / SPEED)], dtype=np.int64)
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gen_text = convert_char_to_pinyin([ref_text + gen_text])
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text_ids = list_str_to_idx(gen_text, vocab_char_map).numpy()
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time_step = np.array([0], dtype=np.int32)
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if "CPUExecutionProvider" in ORT_Accelerate_Providers or not ORT_Accelerate_Providers:
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device_type = 'cpu'
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elif "CUDAExecutionProvider" in ORT_Accelerate_Providers or "TensorrtExecutionProvider" in ORT_Accelerate_Providers:
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device_type = 'cuda'
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elif "DmlExecutionProvider" in ORT_Accelerate_Providers:
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device_type = 'dml'
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else:
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device_type = None
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print("\n\nRun F5-TTS by ONNX Runtime.")
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start_count = time.time()
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noise, rope_cos_q, rope_sin_q, rope_cos_k, rope_sin_k, cat_mel_text, cat_mel_text_drop, ref_signal_len = ort_session_A.run(
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[out_name_A0, out_name_A1, out_name_A2, out_name_A3, out_name_A4, out_name_A5, out_name_A6, out_name_A7],
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{
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in_name_A0: audio,
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in_name_A1: text_ids,
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in_name_A2: max_duration
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})
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if device_type:
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inputs = [
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onnxruntime.OrtValue.ortvalue_from_numpy(noise, device_type, DEVICE_ID),
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onnxruntime.OrtValue.ortvalue_from_numpy(rope_cos_q, device_type, DEVICE_ID),
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onnxruntime.OrtValue.ortvalue_from_numpy(rope_sin_q, device_type, DEVICE_ID),
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onnxruntime.OrtValue.ortvalue_from_numpy(rope_cos_k, device_type, DEVICE_ID),
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onnxruntime.OrtValue.ortvalue_from_numpy(rope_sin_k, device_type, DEVICE_ID),
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onnxruntime.OrtValue.ortvalue_from_numpy(cat_mel_text, device_type, DEVICE_ID),
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onnxruntime.OrtValue.ortvalue_from_numpy(cat_mel_text_drop, device_type, DEVICE_ID),
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onnxruntime.OrtValue.ortvalue_from_numpy(time_step, device_type, DEVICE_ID)
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]
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outputs = [
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inputs[0],
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inputs[-1]
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]
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io_binding = ort_session_B.io_binding()
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for i in range(len(inputs)):
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io_binding.bind_ortvalue_input(
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name=in_name_B[i].name,
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ortvalue=inputs[i]
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)
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for i in range(len(outputs)):
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io_binding.bind_ortvalue_output(
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name=out_name_B[i].name,
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ortvalue=outputs[i]
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)
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print("NFE_STEP: 0")
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for i in range(0, NFE_STEP, FUSE_NFE):
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ort_session_B.run_with_iobinding(io_binding)
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print(f"NFE_STEP: {i + FUSE_NFE}")
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noise = onnxruntime.OrtValue.numpy(io_binding.get_outputs()[0])
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else:
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print("NFE_STEP: 0")
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for i in range(0, NFE_STEP - 1, FUSE_NFE):
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noise, time_step = ort_session_B.run(
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[out_name_B0, out_name_B1],
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{
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in_name_B0: noise,
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in_name_B1: rope_cos_q,
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in_name_B2: rope_sin_q,
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in_name_B3: rope_cos_k,
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in_name_B4: rope_sin_k,
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in_name_B5: cat_mel_text,
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in_name_B6: cat_mel_text_drop,
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in_name_B7: time_step
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})
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print(f"NFE_STEP: {i + FUSE_NFE}")
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generated_signal = ort_session_C.run(
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[out_name_C0],
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{
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in_name_C0: noise,
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in_name_C1: ref_signal_len
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})[0]
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end_count = time.time()
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# Save to audio
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sf.write(generated_audio, generated_signal.reshape(-1), MODEL_SAMPLE_RATE, format='WAVEX')
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print(f"\nAudio generation is complete.\n\nONNXRuntime Time Cost in Seconds:\n{end_count - start_count:.3f}")
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