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import os |
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import logging |
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import sys |
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import numpy as np |
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from modules.devices import devices |
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from modules.synthesize_audio import synthesize_audio |
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from modules.hf import spaces |
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from modules.webui import webui_config |
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logging.basicConfig( |
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level=os.getenv("LOG_LEVEL", "INFO"), |
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", |
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) |
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import gradio as gr |
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import torch |
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from modules.ssml import parse_ssml |
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from modules.SynthesizeSegments import SynthesizeSegments, combine_audio_segments |
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from modules.speaker import speaker_mgr |
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from modules.data import styles_mgr |
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from modules.api.utils import calc_spk_style |
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import modules.generate_audio as generate |
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from modules.normalization import text_normalize |
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from modules import refiner, config |
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from modules.utils import env, audio |
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from modules.SentenceSplitter import SentenceSplitter |
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def get_speakers(): |
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return speaker_mgr.list_speakers() |
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def get_styles(): |
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return styles_mgr.list_items() |
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def segments_length_limit(segments, total_max: int): |
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ret_segments = [] |
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total_len = 0 |
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for seg in segments: |
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if "text" not in seg: |
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continue |
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total_len += len(seg["text"]) |
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if total_len > total_max: |
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break |
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ret_segments.append(seg) |
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return ret_segments |
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@torch.inference_mode() |
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@spaces.GPU |
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def synthesize_ssml(ssml: str, batch_size=4): |
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try: |
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batch_size = int(batch_size) |
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except Exception: |
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batch_size = 8 |
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ssml = ssml.strip() |
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if ssml == "": |
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return None |
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segments = parse_ssml(ssml) |
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max_len = webui_config.ssml_max |
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segments = segments_length_limit(segments, max_len) |
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if len(segments) == 0: |
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return None |
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synthesize = SynthesizeSegments(batch_size=batch_size) |
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audio_segments = synthesize.synthesize_segments(segments) |
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combined_audio = combine_audio_segments(audio_segments) |
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return audio.pydub_to_np(combined_audio) |
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@torch.inference_mode() |
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@spaces.GPU |
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def tts_generate( |
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text, |
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temperature, |
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top_p, |
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top_k, |
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spk, |
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infer_seed, |
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use_decoder, |
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prompt1, |
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prompt2, |
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prefix, |
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style, |
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disable_normalize=False, |
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batch_size=4, |
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): |
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try: |
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batch_size = int(batch_size) |
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except Exception: |
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batch_size = 4 |
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max_len = webui_config.tts_max |
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text = text.strip()[0:max_len] |
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if text == "": |
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return None |
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if style == "*auto": |
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style = None |
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if isinstance(top_k, float): |
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top_k = int(top_k) |
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params = calc_spk_style(spk=spk, style=style) |
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spk = params.get("spk", spk) |
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infer_seed = infer_seed or params.get("seed", infer_seed) |
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temperature = temperature or params.get("temperature", temperature) |
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prefix = prefix or params.get("prefix", prefix) |
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prompt1 = prompt1 or params.get("prompt1", "") |
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prompt2 = prompt2 or params.get("prompt2", "") |
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infer_seed = np.clip(infer_seed, -1, 2**32 - 1, out=None, dtype=np.int64) |
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infer_seed = int(infer_seed) |
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if not disable_normalize: |
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text = text_normalize(text) |
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sample_rate, audio_data = synthesize_audio( |
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text=text, |
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temperature=temperature, |
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top_P=top_p, |
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top_K=top_k, |
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spk=spk, |
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infer_seed=infer_seed, |
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use_decoder=use_decoder, |
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prompt1=prompt1, |
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prompt2=prompt2, |
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prefix=prefix, |
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batch_size=batch_size, |
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) |
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audio_data = audio.audio_to_int16(audio_data) |
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return sample_rate, audio_data |
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@torch.inference_mode() |
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@spaces.GPU |
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def refine_text(text: str, prompt: str): |
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text = text_normalize(text) |
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return refiner.refine_text(text, prompt=prompt) |
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@torch.inference_mode() |
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@spaces.GPU |
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def split_long_text(long_text_input): |
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spliter = SentenceSplitter(webui_config.spliter_threshold) |
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sentences = spliter.parse(long_text_input) |
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sentences = [text_normalize(s) for s in sentences] |
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data = [] |
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for i, text in enumerate(sentences): |
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data.append([i, text, len(text)]) |
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return data |
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