mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-09-29 00:30:15 +08:00
131 lines
4.6 KiB
Python
131 lines
4.6 KiB
Python
import onnxruntime as ort
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import numpy as np
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import onnx
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from tqdm import tqdm
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import torchaudio
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import torch
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from TTS_infer_pack.TextPreprocessor_onnx import TextPreprocessorOnnx
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MODEL_PATH = "onnx/v2_export/v2"
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def audio_postprocess(
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audios,
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fragment_interval: float = 0.3,
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):
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zero_wav = np.zeros((int(32000 * fragment_interval),)).astype(np.float32)
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for i, audio in enumerate(audios):
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max_audio = np.abs(audio).max() # 简单防止16bit爆音
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if max_audio > 1:
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audio /= max_audio
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audio = np.concatenate([audio, zero_wav], axis=0)
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audios[i] = audio
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audio = np.concatenate(audios, axis=0)
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# audio = (audio * 32768).astype(np.int16)
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audio_tensor = torch.from_numpy(audio).unsqueeze(0)
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torchaudio.save('playground/output.wav', audio_tensor, 32000)
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return audio
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def load_audio(audio_path):
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"""Load and preprocess audio file to 32k"""
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waveform, sample_rate = torchaudio.load(audio_path)
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# Resample to 32kHz if needed
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if sample_rate != 32000:
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resampler = torchaudio.transforms.Resample(sample_rate, 32000)
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waveform = resampler(waveform)
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# Take first channel
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if waveform.shape[0] > 1:
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waveform = waveform[0:1]
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return waveform
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def audio_preprocess(audio_path):
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"""Get HuBERT features for the audio file"""
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waveform = load_audio(audio_path)
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ort_session = ort.InferenceSession(MODEL_PATH + "_export_audio_preprocess.onnx")
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ort_inputs = {ort_session.get_inputs()[0].name: waveform.numpy()}
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[hubert_feature, spectrum, sv_emb] = ort_session.run(None, ort_inputs)
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return hubert_feature, spectrum, sv_emb
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def preprocess_text(text:str):
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preprocessor = TextPreprocessorOnnx("playground/bert")
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[phones, bert_features, norm_text] = preprocessor.segment_and_extract_feature_for_text(text, 'all_zh', 'v2')
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phones = np.expand_dims(np.array(phones, dtype=np.int64), axis=0)
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return phones, bert_features.T.astype(np.float32)
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# input_phones_saved = np.load("playground/ref/input_phones.npy")
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# input_bert_saved = np.load("playground/ref/input_bert.npy").T.astype(np.float32)
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[input_phones, input_bert] = preprocess_text("天上的风筝在天上飞，地上的人儿在地上追。")
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# ref_phones = np.load("playground/ref/ref_phones.npy")
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# ref_bert = np.load("playground/ref/ref_bert.npy").T.astype(np.float32)
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[ref_phones, ref_bert] = preprocess_text("今日江苏苏州荷花市集开张热闹与浪漫交织")
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[audio_prompt_hubert, spectrum, sv_emb] = audio_preprocess("playground/ref/audio.wav")
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np.save("playground/ref/audio_prompt_hubert.npy", audio_prompt_hubert.astype(np.float16))
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# audio_prompt_hubert_saved = np.load("playground/ref/audio_prompt_hubert.npy").astype(np.float32)
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t2s_combined = ort.InferenceSession(MODEL_PATH+"_export_t2s_combined.onnx")
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# t2s_init_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_init_step.onnx")
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[y, k, v, y_emb, x_example, fake_logits, fake_samples] = t2s_combined.run(None, {
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"if_init_step": np.array(True, dtype=bool),
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"input_text_phones": input_phones,
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"input_text_bert": input_bert,
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"ref_text_phones": ref_phones,
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"ref_text_bert": ref_bert,
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"hubert_ssl_content": audio_prompt_hubert,
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"iy":np.empty((1, 0), dtype=np.int64),
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"ik":np.empty((24, 0, 1, 512), dtype=np.float32),
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"iv":np.empty((24, 0, 1, 512), dtype=np.float32),
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"iy_emb":np.empty((1, 0, 512), dtype=np.float32),
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"ix_example":np.empty((1, 0), dtype=np.float32)
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})
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# t2s_stage_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_sdec.onnx")
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for idx in tqdm(range(1, 1500)):
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# [1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
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[y, k, v, y_emb, fake_x_example, logits, samples] = t2s_combined.run(None, {
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"if_init_step": np.array(False, dtype=bool),
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"input_text_phones": np.empty((1, 0), dtype=np.int64),
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"input_text_bert": np.empty((0, 1024), dtype=np.float32),
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"ref_text_phones": np.empty((1, 0), dtype=np.int64),
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"ref_text_bert": np.empty((0, 1024), dtype=np.float32),
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"hubert_ssl_content": np.empty((1, 768, 0), dtype=np.float32),
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"iy": y,
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"ik": k,
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"iv": v,
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"iy_emb": y_emb,
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"ix_example": x_example
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})
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if np.argmax(logits, axis=-1)[0] == 1024 or samples[0, 0] == 1024: # 1024 is the EOS token
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break
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y[0, -1] = 0
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pred_semantic = np.expand_dims(y[:, -idx:], axis=0)
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vtis = ort.InferenceSession(MODEL_PATH+"_export_vits.onnx")
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[audio] = vtis.run(None, {
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"input_text_phones": input_phones,
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"pred_semantic": pred_semantic,
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"spectrum": spectrum.astype(np.float32),
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# "sv_emb": sv_emb.astype(np.float32)
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})
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audio_postprocess([audio])
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