add new params

GPT_SoVITS/inference_cli.py

@@ -7,7 +7,8 @@ from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights
 
 i18n = I18nAuto()
 
-def synthesize(GPT_model_path, SoVITS_model_path, ref_audio_path, ref_text, ref_language, target_text, text_language, output_path):
+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, 
+               energy_scale=1.0, volume_scale=1.0, strain_effect=0.0):
     
     # Change model weights
     change_gpt_weights(gpt_path=GPT_model_path)
@@ -18,7 +19,15 @@ def synthesize(GPT_model_path, SoVITS_model_path, ref_audio_path, ref_text, ref_
                                    prompt_text=ref_text, 
                                    prompt_language=i18n(ref_language), 
                                    text=target_text, 
-                                   text_language=i18n(text_language), top_p=1, temperature=1)
+                                   text_language=i18n(text_language), 
+                                   top_p=1, 
+                                   temperature=1,
+                                   loudness_boost=loudness_boost, 
+                                   gain=gain, 
+                                   normalize=normalize, 
+                                   energy_scale=energy_scale, 
+                                   volume_scale=volume_scale, 
+                                   strain_effect=strain_effect)
     
     result_list = list(synthesis_result)
 

GPT_SoVITS/inference_webui.py

@@ -510,24 +510,27 @@ def audio_sr(audio,sr):
 ##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
 # cache_tokens={}#暂未实现清理机制
 cache= {}
-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):
+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, 
+                loudness_boost=False, gain=0, normalize=False, energy_scale=1.0, 
+                volume_scale=1.0, strain_effect=0.0):
     global cache
-    if ref_wav_path:pass
+    if ref_wav_path: pass
-    else:gr.Warning(i18n('请上传参考音频'))
+    else: gr.Warning(i18n('请上传参考音频'))
-    if text:pass
+    if text: pass
-    else:gr.Warning(i18n('请填入推理文本'))
+    else: gr.Warning(i18n('请填入推理文本'))
     t = []
     if prompt_text is None or len(prompt_text) == 0:
         ref_free = True
-    if model_version=="v3":
+    if model_version == "v3":
-        ref_free=False#s2v3暂不支持ref_free
+        ref_free = False  # s2v3暂不支持ref_free
     else:
-        if_sr=False
+        if_sr = False
     t0 = ttime()
     prompt_language = dict_language[prompt_language]
     text_language = dict_language[text_language]
 
-
     if not ref_free:
         prompt_text = prompt_text.strip("\n")
         if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
@@ -567,7 +570,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
             prompt = prompt_semantic.unsqueeze(0).to(device)
 
     t1 = ttime()
-    t.append(t1-t0)
+    t.append(t1 - t0)
 
     if (how_to_cut == i18n("凑四句一切")):
         text = cut1(text)
@@ -586,21 +589,21 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
     texts = process_text(texts)
     texts = merge_short_text_in_array(texts, 5)
     audio_opt = []
-    ###s2v3暂不支持ref_free
+    ### s2v3暂不支持ref_free
     if not ref_free:
-        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language, version)
+        phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
 
-    for i_text,text in enumerate(texts):
+    for i_text, text in enumerate(texts):
         # 解决输入目标文本的空行导致报错的问题
         if (len(text.strip()) == 0):
             continue
         if (text[-1] not in splits): text += "。" if text_language != "en" else "."
         print(i18n("实际输入的目标文本(每句):"), text)
-        phones2,bert2,norm_text2=get_phones_and_bert(text, text_language, version)
+        phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
         print(i18n("前端处理后的文本(每句):"), norm_text2)
         if not ref_free:
             bert = torch.cat([bert1, bert2], 1)
-            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
+            all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
         else:
             bert = bert2
             all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
@@ -611,7 +614,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
         t2 = ttime()
         # cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
         # print(cache.keys(),if_freeze)
-        if(i_text in cache and if_freeze==True):pred_semantic=cache[i_text]
+        if(i_text in cache and if_freeze == True): pred_semantic = cache[i_text]
         else:
             with torch.no_grad():
                 pred_semantic, idx = t2s_model.model.infer_panel(
@@ -626,33 +629,33 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
                     early_stop_num=hz * max_sec,
                 )
                 pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
-                cache[i_text]=pred_semantic
+                cache[i_text] = pred_semantic
         t3 = ttime()
-        ###v3不存在以下逻辑和inp_refs
+        ### v3不存在以下逻辑和inp_refs
-        if model_version!="v3":
+        if model_version != "v3":
-            refers=[]
+            refers = []
-            if(inp_refs):
+            if inp_refs:
                 for path in inp_refs:
                     try:
                         refer = get_spepc(hps, path.name).to(dtype).to(device)
                         refers.append(refer)
                     except:
                         traceback.print_exc()
-            if(len(refers)==0):refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
+            if len(refers) == 0: refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
-            audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers,speed=speed)[0][0]#.cpu().detach().numpy()
+            audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed)[0][0]  # .cpu().detach().numpy()
         else:
             refer = get_spepc(hps, ref_wav_path).to(device).to(dtype)
-            phoneme_ids0=torch.LongTensor(phones1).to(device).unsqueeze(0)
+            phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
-            phoneme_ids1=torch.LongTensor(phones2).to(device).unsqueeze(0)
+            phoneme_ids1 = torch.LongTensor(phones2).to(device).unsqueeze(0)
             # print(11111111, phoneme_ids0, phoneme_ids1)
-            fea_ref,ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)
+            fea_ref, ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)
             ref_audio, sr = torchaudio.load(ref_wav_path)
-            ref_audio=ref_audio.to(device).float()
+            ref_audio = ref_audio.to(device).float()
             if (ref_audio.shape[0] == 2):
                 ref_audio = ref_audio.mean(0).unsqueeze(0)
-            if sr!=24000:
+            if sr != 24000:
-                ref_audio=resample(ref_audio,sr)
+                ref_audio = resample(ref_audio, sr)
-            # print("ref_audio",ref_audio.abs().mean())
+            # print("ref_audio", ref_audio.abs().mean())
             mel2 = mel_fn(ref_audio)
             mel2 = norm_spec(mel2)
             T_min = min(mel2.shape[2], fea_ref.shape[2])
@@ -663,12 +666,12 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
                 fea_ref = fea_ref[:, :, -468:]
                 T_min = 468
             chunk_len = 934 - T_min
-            # print("fea_ref",fea_ref,fea_ref.shape)
+            # print("fea_ref", fea_ref, fea_ref.shape)
-            # print("mel2",mel2)
+            # print("mel2", mel2)
-            mel2=mel2.to(dtype)
+            mel2 = mel2.to(dtype)
-            fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge,speed)
+            fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge, speed)
-            # print("fea_todo",fea_todo)
+            # print("fea_todo", fea_todo)
-            # print("ge",ge.abs().mean())
+            # print("ge", ge.abs().mean())
             cfm_resss = []
             idx = 0
             while (1):
@@ -686,30 +689,62 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
                 cfm_resss.append(cfm_res)
             cmf_res = torch.cat(cfm_resss, 2)
             cmf_res = denorm_spec(cmf_res)
-            if bigvgan_model==None:init_bigvgan()
+            if bigvgan_model == None: init_bigvgan()
             with torch.inference_mode():
                 wav_gen = bigvgan_model(cmf_res)
-                audio=wav_gen[0][0]#.cpu().detach().numpy()
+                audio = wav_gen[0][0]  # .cpu().detach().numpy()
-        max_audio=torch.abs(audio).max()#简单防止16bit爆音
+
-        if max_audio>1:audio=audio/max_audio
+        # Initial clipping check
+        max_audio = torch.abs(audio).max()  # 简单防止16bit爆音
+        if max_audio > 1:
+            audio = audio / max_audio
+
+        # Apply new parameters
+        audio = audio.to(torch.float32)  # Ensure float32 for processing
+        if loudness_boost:
+            # Boost loudness using RMS-based scaling (adjust multiplier as needed)
+            rms = torch.sqrt(torch.mean(audio ** 2))
+            audio = audio * (rms * 1.5) if rms > 0 else audio
+        if gain > 0:
+            # Apply gain in dB
+            audio = audio * (10 ** (gain / 20))
+        if normalize:
+            # Normalize to [-1, 1]
+            max_abs = torch.abs(audio).max()
+            audio = audio / max_abs if max_abs > 0 else audio
+        if energy_scale != 1.0:
+            # Scale energy
+            audio = audio * torch.sqrt(torch.tensor(energy_scale))
+        if volume_scale != 1.0:
+            # Direct volume scaling
+            audio = audio * volume_scale
+        if strain_effect > 0.0:
+            # Add strain effect (basic distortion)
+            audio = audio + (audio ** 2 * strain_effect)
+
+        # Final clipping check after effects
+        max_audio = torch.abs(audio).max()
+        if max_audio > 1:
+            audio = audio / max_audio
+
         audio_opt.append(audio)
-        audio_opt.append(zero_wav_torch)#zero_wav
+        audio_opt.append(zero_wav_torch)  # zero_wav
         t4 = ttime()
-        t.extend([t2 - t1,t3 - t2, t4 - t3])
+        t.extend([t2 - t1, t3 - t2, t4 - t3])
         t1 = ttime()
+
     print("%.3f\t%.3f\t%.3f\t%.3f" % (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3])))
-    audio_opt=torch.cat(audio_opt, 0)#np.concatenate
+    audio_opt = torch.cat(audio_opt, 0)  # np.concatenate
-    sr=hps.data.sampling_rate if model_version!="v3"else 24000
+    sr = hps.data.sampling_rate if model_version != "v3" else 24000
-    if if_sr==True and sr==24000:
+    if if_sr == True and sr == 24000:
         print(i18n("音频超分中"))
-        audio_opt,sr=audio_sr(audio_opt.unsqueeze(0),sr)
+        audio_opt, sr = audio_sr(audio_opt.unsqueeze(0), sr)
-        max_audio=np.abs(audio_opt).max()
+        max_audio = np.abs(audio_opt).max()
         if max_audio > 1: audio_opt /= max_audio
     else:
-        audio_opt=audio_opt.cpu().detach().numpy()
+        audio_opt = audio_opt.cpu().detach().numpy()
     yield sr, (audio_opt * 32767).astype(np.int16)
 
-
 def split(todo_text):
     todo_text = todo_text.replace("……", "。").replace("——", "，")
     if todo_text[-1] not in splits:

api.py

@@ -1158,6 +1158,12 @@ def version_4_cli(
     character_name: str = "Kurari",
     model_id: int = 14,
     version: str = "v1",  # v3 or v4
+    loudness_boost=False,
+    gain=0,
+    normalize=False,
+    energy_scale=1.0,
+    volume_scale=1.0,
+    strain_effect=0.0,
 ):
     # Create a temporary buffer to store the audio
     audio_buffer = io.BytesIO()
@@ -1187,7 +1193,13 @@ def version_4_cli(
         ref_language = ref_language,
         target_text = target_text,
         text_language = text_language,
-        output_path = output_path  # Don't save to file
+        output_path = output_path,  # Don't save to file
+        loudness_boost=loudness_boost,
+        gain=gain,
+        normalize=normalize,
+        energy_scale=energy_scale,
+        volume_scale=volume_scale,
+        strain_effect=strain_effect
     )
     
     # Get the last audio data and sample rate from synthesis result