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import numpy as np |
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from fastapi import HTTPException |
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from modules.api.impl.handler.AudioHandler import AudioHandler |
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from modules.api.impl.model.audio_model import AdjustConfig |
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from modules.api.impl.model.chattts_model import InferConfig |
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from modules.api.impl.model.enhancer_model import EnhancerConfig |
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from modules.Enhancer.ResembleEnhance import apply_audio_enhance_full |
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from modules.normalization import text_normalize |
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from modules.ssml_parser.SSMLParser import create_ssml_parser |
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from modules.SynthesizeSegments import SynthesizeSegments, combine_audio_segments |
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from modules.utils import audio |
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class SSMLHandler(AudioHandler): |
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def __init__( |
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self, |
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ssml_content: str, |
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infer_config: InferConfig, |
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adjust_config: AdjustConfig, |
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enhancer_config: EnhancerConfig, |
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) -> None: |
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assert isinstance(ssml_content, str), "ssml_content must be a string." |
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assert isinstance( |
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infer_config, InferConfig |
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), "infer_config must be an InferConfig object." |
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assert isinstance( |
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adjust_config, AdjustConfig |
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), "adjest_config should be AdjustConfig" |
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assert isinstance( |
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enhancer_config, EnhancerConfig |
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), "enhancer_config must be an EnhancerConfig object." |
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self.ssml_content = ssml_content |
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self.infer_config = infer_config |
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self.adjest_config = adjust_config |
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self.enhancer_config = enhancer_config |
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self.validate() |
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def validate(self): |
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pass |
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def enqueue(self) -> tuple[np.ndarray, int]: |
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ssml_content = self.ssml_content |
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infer_config = self.infer_config |
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adjust_config = self.adjest_config |
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enhancer_config = self.enhancer_config |
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parser = create_ssml_parser() |
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segments = parser.parse(ssml_content) |
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for seg in segments: |
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seg["text"] = text_normalize(seg["text"], is_end=True) |
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if len(segments) == 0: |
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raise HTTPException( |
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status_code=422, detail="The SSML text is empty or parsing failed." |
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) |
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synthesize = SynthesizeSegments( |
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batch_size=infer_config.batch_size, |
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eos=infer_config.eos, |
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spliter_thr=infer_config.spliter_threshold, |
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) |
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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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sample_rate, audio_data = audio.pydub_to_np(combined_audio) |
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if enhancer_config.enabled: |
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nfe = enhancer_config.nfe |
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solver = enhancer_config.solver |
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lambd = enhancer_config.lambd |
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tau = enhancer_config.tau |
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audio_data, sample_rate = apply_audio_enhance_full( |
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audio_data=audio_data, |
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sr=sample_rate, |
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nfe=nfe, |
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solver=solver, |
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lambd=lambd, |
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tau=tau, |
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) |
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audio_data = audio.apply_prosody_to_audio_data( |
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audio_data=audio_data, |
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rate=adjust_config.speed_rate, |
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pitch=adjust_config.pitch, |
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volume=adjust_config.volume_gain_db, |
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sr=sample_rate, |
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) |
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return audio_data, sample_rate |
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