import numpy as np from pydub import AudioSegment from typing import Any, List, Dict from scipy.io.wavfile import write import io from modules.utils.audio import time_stretch, pitch_shift from modules import generate_audio from modules.normalization import text_normalize import logging import json import random import copy from modules.speaker import Speaker logger = logging.getLogger(__name__) def audio_data_to_segment(audio_data, sr): byte_io = io.BytesIO() write(byte_io, rate=sr, data=audio_data) byte_io.seek(0) return AudioSegment.from_file(byte_io, format="wav") def combine_audio_segments(audio_segments: list) -> AudioSegment: combined_audio = AudioSegment.empty() for segment in audio_segments: combined_audio += segment return combined_audio def apply_prosody( audio_segment: AudioSegment, rate: float, volume: float, pitch: float ) -> AudioSegment: if rate != 1: audio_segment = time_stretch(audio_segment, rate) if volume != 0: audio_segment += volume if pitch != 0: audio_segment = pitch_shift(audio_segment, pitch) return audio_segment def to_number(value, t, default=0): try: number = t(value) return number except (ValueError, TypeError) as e: return default class SynthesizeSegments: batch_default_spk_seed = int(np.random.randint(0, 2**32 - 1)) batch_default_infer_seed = int(np.random.randint(0, 2**32 - 1)) def __init__(self, batch_size: int = 8): self.batch_size = batch_size def segment_to_generate_params(self, segment: Dict[str, Any]) -> Dict[str, Any]: if segment.get("params", None) is not None: return segment["params"] text = segment.get("text", "") is_end = segment.get("is_end", False) text = str(text).strip() attrs = segment.get("attrs", {}) spk = attrs.get("spk", "") if isinstance(spk, str): spk = int(spk) seed = to_number(attrs.get("seed", ""), int, -1) top_k = to_number(attrs.get("top_k", ""), int, None) top_p = to_number(attrs.get("top_p", ""), float, None) temp = to_number(attrs.get("temp", ""), float, None) prompt1 = attrs.get("prompt1", "") prompt2 = attrs.get("prompt2", "") prefix = attrs.get("prefix", "") disable_normalize = attrs.get("normalize", "") == "False" params = { "text": text, "temperature": temp if temp is not None else 0.3, "top_P": top_p if top_p is not None else 0.5, "top_K": top_k if top_k is not None else 20, "spk": spk if spk else -1, "infer_seed": seed if seed else -1, "prompt1": prompt1 if prompt1 else "", "prompt2": prompt2 if prompt2 else "", "prefix": prefix if prefix else "", } if not disable_normalize: params["text"] = text_normalize(text, is_end=is_end) # Set default values for spk and infer_seed if params["spk"] == -1: params["spk"] = self.batch_default_spk_seed if params["infer_seed"] == -1: params["infer_seed"] = self.batch_default_infer_seed return params def bucket_segments( self, segments: List[Dict[str, Any]] ) -> List[List[Dict[str, Any]]]: # Create a dictionary to hold buckets buckets = {} for segment in segments: params = self.segment_to_generate_params(segment) key_params = copy.copy(params) if isinstance(key_params.get("spk"), Speaker): key_params["spk"] = str(key_params["spk"].id) key = json.dumps( {k: v for k, v in key_params.items() if k != "text"}, sort_keys=True ) if key not in buckets: buckets[key] = [] buckets[key].append(segment) # Convert dictionary to list of buckets bucket_list = list(buckets.values()) return bucket_list def synthesize_segments(self, segments: List[Dict[str, Any]]) -> List[AudioSegment]: audio_segments = [None] * len( segments ) # Create a list with the same length as segments buckets = self.bucket_segments(segments) logger.debug(f"segments len: {len(segments)}") logger.debug(f"bucket pool size: {len(buckets)}") for bucket in buckets: for i in range(0, len(bucket), self.batch_size): batch = bucket[i : i + self.batch_size] param_arr = [ self.segment_to_generate_params(segment) for segment in batch ] texts = [params["text"] for params in param_arr] params = param_arr[0] # Use the first segment to get the parameters audio_datas = generate_audio.generate_audio_batch( texts=texts, temperature=params["temperature"], top_P=params["top_P"], top_K=params["top_K"], spk=params["spk"], infer_seed=params["infer_seed"], prompt1=params["prompt1"], prompt2=params["prompt2"], prefix=params["prefix"], ) for idx, segment in enumerate(batch): (sr, audio_data) = audio_datas[idx] rate = float(segment.get("rate", "1.0")) volume = float(segment.get("volume", "0")) pitch = float(segment.get("pitch", "0")) audio_segment = audio_data_to_segment(audio_data, sr) audio_segment = apply_prosody(audio_segment, rate, volume, pitch) original_index = segments.index( segment ) # Get the original index of the segment audio_segments[original_index] = ( audio_segment # Place the audio_segment in the correct position ) return audio_segments def generate_audio_segment( text: str, spk: int = -1, seed: int = -1, top_p: float = 0.5, top_k: int = 20, temp: float = 0.3, prompt1: str = "", prompt2: str = "", prefix: str = "", enable_normalize=True, is_end: bool = False, ) -> AudioSegment: if enable_normalize: text = text_normalize(text, is_end=is_end) logger.debug(f"generate segment: {text}") sample_rate, audio_data = generate_audio.generate_audio( text=text, temperature=temp if temp is not None else 0.3, top_P=top_p if top_p is not None else 0.5, top_K=top_k if top_k is not None else 20, spk=spk if spk else -1, infer_seed=seed if seed else -1, prompt1=prompt1 if prompt1 else "", prompt2=prompt2 if prompt2 else "", prefix=prefix if prefix else "", ) byte_io = io.BytesIO() write(byte_io, sample_rate, audio_data) byte_io.seek(0) return AudioSegment.from_file(byte_io, format="wav") def synthesize_segment(segment: Dict[str, Any]) -> AudioSegment | None: if "break" in segment: pause_segment = AudioSegment.silent(duration=segment["break"]) return pause_segment attrs = segment.get("attrs", {}) text = segment.get("text", "") is_end = segment.get("is_end", False) text = str(text).strip() if text == "": return None spk = attrs.get("spk", "") if isinstance(spk, str): spk = int(spk) seed = to_number(attrs.get("seed", ""), int, -1) top_k = to_number(attrs.get("top_k", ""), int, None) top_p = to_number(attrs.get("top_p", ""), float, None) temp = to_number(attrs.get("temp", ""), float, None) prompt1 = attrs.get("prompt1", "") prompt2 = attrs.get("prompt2", "") prefix = attrs.get("prefix", "") disable_normalize = attrs.get("normalize", "") == "False" audio_segment = generate_audio_segment( text, enable_normalize=not disable_normalize, spk=spk, seed=seed, top_k=top_k, top_p=top_p, temp=temp, prompt1=prompt1, prompt2=prompt2, prefix=prefix, is_end=is_end, ) rate = float(attrs.get("rate", "1.0")) volume = float(attrs.get("volume", "0")) pitch = float(attrs.get("pitch", "0")) audio_segment = apply_prosody(audio_segment, rate, volume, pitch) return audio_segment # 示例使用 if __name__ == "__main__": ssml_segments = [ { "text": "大🍌，一条大🍌，嘿，你的感觉真的很奇妙 [lbreak]", "attrs": {"spk": 2, "temp": 0.1, "seed": 42}, }, { "text": "大🍉，一个大🍉，嘿，你的感觉真的很奇妙 [lbreak]", "attrs": {"spk": 2, "temp": 0.1, "seed": 42}, }, { "text": "大🍌，一条大🍌，嘿，你的感觉真的很奇妙 [lbreak]", "attrs": {"spk": 2, "temp": 0.3, "seed": 42}, }, ] synthesizer = SynthesizeSegments(batch_size=2) audio_segments = synthesizer.synthesize_segments(ssml_segments) combined_audio = combine_audio_segments(audio_segments) combined_audio.export("output.wav", format="wav")