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https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-08-15 05:21:57 +08:00
add new params
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samiabat
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@ -7,7 +7,8 @@ from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights
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i18n = I18nAuto()
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def synthesize(GPT_model_path, SoVITS_model_path, ref_audio_path, ref_text, ref_language, target_text, text_language, output_path):
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def synthesize(GPT_model_path, SoVITS_model_path, ref_audio_path, ref_text, ref_language, target_text, text_language, output_path, loudness_boost=False, gain=0, normalize=False,
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energy_scale=1.0, volume_scale=1.0, strain_effect=0.0):
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# Change model weights
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change_gpt_weights(gpt_path=GPT_model_path)
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@ -18,7 +19,15 @@ def synthesize(GPT_model_path, SoVITS_model_path, ref_audio_path, ref_text, ref_
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prompt_text=ref_text,
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prompt_language=i18n(ref_language),
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text=target_text,
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text_language=i18n(text_language), top_p=1, temperature=1)
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text_language=i18n(text_language),
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top_p=1,
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temperature=1,
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loudness_boost=loudness_boost,
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gain=gain,
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normalize=normalize,
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energy_scale=energy_scale,
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volume_scale=volume_scale,
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strain_effect=strain_effect)
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result_list = list(synthesis_result)
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@ -510,7 +510,11 @@ def audio_sr(audio,sr):
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##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
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# cache_tokens={}#暂未实现清理机制
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cache= {}
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=15, top_p=1, temperature=1, ref_free = False,speed=1,if_freeze=False,inp_refs=None,sample_steps=16,if_sr=False,pause_second=0.3):
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"),
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top_k=15, top_p=1, temperature=1, ref_free=False, speed=1, if_freeze=False,
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inp_refs=None, sample_steps=16, if_sr=False, pause_second=0.3,
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loudness_boost=False, gain=0, normalize=False, energy_scale=1.0,
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volume_scale=1.0, strain_effect=0.0):
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global cache
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if ref_wav_path: pass
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else: gr.Warning(i18n('请上传参考音频'))
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@ -527,7 +531,6 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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prompt_language = dict_language[prompt_language]
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text_language = dict_language[text_language]
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if not ref_free:
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prompt_text = prompt_text.strip("\n")
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if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
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@ -631,14 +634,14 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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### v3不存在以下逻辑和inp_refs
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if model_version != "v3":
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refers = []
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if(inp_refs):
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if inp_refs:
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for path in inp_refs:
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try:
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refer = get_spepc(hps, path.name).to(dtype).to(device)
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refers.append(refer)
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except:
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traceback.print_exc()
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if(len(refers)==0):refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
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if len(refers) == 0: refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
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audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed)[0][0] # .cpu().detach().numpy()
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else:
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refer = get_spepc(hps, ref_wav_path).to(device).to(dtype)
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@ -690,13 +693,46 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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with torch.inference_mode():
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wav_gen = bigvgan_model(cmf_res)
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audio = wav_gen[0][0] # .cpu().detach().numpy()
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# Initial clipping check
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max_audio = torch.abs(audio).max() # 简单防止16bit爆音
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if max_audio>1:audio=audio/max_audio
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if max_audio > 1:
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audio = audio / max_audio
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# Apply new parameters
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audio = audio.to(torch.float32) # Ensure float32 for processing
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if loudness_boost:
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# Boost loudness using RMS-based scaling (adjust multiplier as needed)
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rms = torch.sqrt(torch.mean(audio ** 2))
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audio = audio * (rms * 1.5) if rms > 0 else audio
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if gain > 0:
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# Apply gain in dB
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audio = audio * (10 ** (gain / 20))
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if normalize:
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# Normalize to [-1, 1]
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max_abs = torch.abs(audio).max()
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audio = audio / max_abs if max_abs > 0 else audio
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if energy_scale != 1.0:
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# Scale energy
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audio = audio * torch.sqrt(torch.tensor(energy_scale))
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if volume_scale != 1.0:
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# Direct volume scaling
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audio = audio * volume_scale
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if strain_effect > 0.0:
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# Add strain effect (basic distortion)
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audio = audio + (audio ** 2 * strain_effect)
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# Final clipping check after effects
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max_audio = torch.abs(audio).max()
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if max_audio > 1:
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audio = audio / max_audio
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audio_opt.append(audio)
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audio_opt.append(zero_wav_torch) # zero_wav
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t4 = ttime()
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t.extend([t2 - t1, t3 - t2, t4 - t3])
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t1 = ttime()
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3])))
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audio_opt = torch.cat(audio_opt, 0) # np.concatenate
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sr = hps.data.sampling_rate if model_version != "v3" else 24000
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@ -709,7 +745,6 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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audio_opt = audio_opt.cpu().detach().numpy()
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yield sr, (audio_opt * 32767).astype(np.int16)
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def split(todo_text):
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todo_text = todo_text.replace("……", "。").replace("——", "，")
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if todo_text[-1] not in splits:
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14
api.py
14
api.py
@ -1158,6 +1158,12 @@ def version_4_cli(
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character_name: str = "Kurari",
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model_id: int = 14,
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version: str = "v1", # v3 or v4
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loudness_boost=False,
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gain=0,
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normalize=False,
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energy_scale=1.0,
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volume_scale=1.0,
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strain_effect=0.0,
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):
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# Create a temporary buffer to store the audio
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audio_buffer = io.BytesIO()
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@ -1187,7 +1193,13 @@ def version_4_cli(
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ref_language = ref_language,
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target_text = target_text,
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text_language = text_language,
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output_path = output_path # Don't save to file
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output_path = output_path, # Don't save to file
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loudness_boost=loudness_boost,
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gain=gain,
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normalize=normalize,
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energy_scale=energy_scale,
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volume_scale=volume_scale,
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strain_effect=strain_effect
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)
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# Get the last audio data and sample rate from synthesis result
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