feat: v2pro and v2proplus onnx export works, todo:solve data dependencies

.gitignore

@@ -194,3 +194,4 @@ cython_debug/
 # PyPI configuration file
 .pypirc
 onnx/
+*.onnx

GPT_SoVITS/onnx_export.py

@@ -1,4 +1,5 @@
 import torch
+import torch.nn.functional as F
 import torchaudio
 from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
 from feature_extractor import cnhubert
@@ -39,6 +40,27 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
     return spec
 
+def resample_audio(audio: torch.Tensor, orig_sr: int, target_sr: int) -> torch.Tensor:
+    """
+    Resample audio from orig_sr to target_sr using linear interpolation.
+    audio: (batch, channels, samples) or (channels, samples) or (samples,)
+    """
+    if audio.dim() == 1:
+        audio = audio.unsqueeze(0).unsqueeze(0)
+    elif audio.dim() == 2:
+        audio = audio.unsqueeze(0)
+    # audio shape: (batch, channels, samples)
+    batch, channels, samples = audio.shape
+    new_samples = int(samples * target_sr / orig_sr)
+    audio = audio.view(batch * channels, 1, samples)
+    resampled = F.interpolate(audio, size=new_samples, mode='linear', align_corners=False)
+    resampled = resampled.view(batch, channels, new_samples)
+    if resampled.shape[0] == 1 and resampled.shape[1] == 1:
+        resampled = resampled.squeeze(0).squeeze(0)
+    elif resampled.shape[0] == 1:
+        resampled = resampled.squeeze(0)
+    return resampled
+
 
 class DictToAttrRecursive(dict):
     def __init__(self, input_dict):
@@ -225,7 +247,7 @@ class VitsModel(nn.Module):
             center=False,
         )
         if self.sv_model is not None:
-            sv_emb=self.sv_model.compute_embedding3_onnx(ref_audio)
+            sv_emb=self.sv_model.compute_embedding3_onnx(resample_audio(ref_audio, 32000, 16000))
             return self.vq_model(pred_semantic, text_seq, refer, sv_emb=sv_emb)[0, 0]
         return self.vq_model(pred_semantic, text_seq, refer)[0, 0]
 
@@ -292,7 +314,7 @@ def export(vits_path, gpt_path, project_name, voice_model_version="v2"):
                     "y",
                     "e4",
                 ],
-                version=voice_model_version,
+                version='v2',
             )
         ]
     )
@@ -325,7 +347,7 @@ def export(vits_path, gpt_path, project_name, voice_model_version="v2"):
                     "y",
                     "e4",
                 ],
-                version=voice_model_version,
+                version='v2',
             )
         ]
     )
@@ -380,6 +402,11 @@ if __name__ == "__main__":
     # version = "v2"
     # export(vits_path, gpt_path, exp_path, version)
 
+    # gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
+    # vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
+    # exp_path = "v2pro_export"
+    # version = "v2Pro"
+
     gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
     vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
     exp_path = "v2proplus_export"

playground/freerun.py

@@ -0,0 +1,102 @@
+import onnxruntime as ort
+import numpy as np
+import onnx
+from tqdm import tqdm
+import torchaudio
+import torch
+
+MODEL_PATH = "playground/v2proplus_export/v2proplus"
+
+def audio_postprocess(
+    audios,
+    fragment_interval: float = 0.3,
+):
+    zero_wav = np.zeros((int(32000 * fragment_interval),)).astype(np.float32)
+    for i, audio in enumerate(audios):
+        max_audio = np.abs(audio).max()  # 简单防止16bit爆音
+        if max_audio > 1:
+            audio /= max_audio
+        audio = np.concatenate([audio, zero_wav], axis=0)
+        audios[i] = audio
+
+    audio = np.concatenate(audios, axis=0)
+
+    # audio = (audio * 32768).astype(np.int16)
+
+    audio_tensor = torch.from_numpy(audio).unsqueeze(0)
+
+    torchaudio.save('playground/output.wav', audio_tensor, 32000)
+
+    return audio
+
+
+input_phones = np.load("playground/input_phones.npy")
+input_bert = np.load("playground/input_bert.npy").T.astype(np.float32)
+ref_phones = np.load("playground/ref_phones.npy")
+ref_bert = np.load("playground/ref_bert.npy").T.astype(np.float32)
+audio_prompt_hubert = np.load("playground/audio_prompt_hubert.npy").astype(np.float32)
+
+
+encoder = ort.InferenceSession(MODEL_PATH+"_export_t2s_encoder.onnx")
+
+outputs = encoder.run(None, {
+    "text_seq": input_phones,
+    "text_bert": input_bert,
+    "ref_seq": ref_phones,
+    "ref_bert": ref_bert,
+    "ssl_content": audio_prompt_hubert
+})
+
+print(outputs[0].shape, outputs[1].shape)
+
+x = outputs[0]
+prompts = outputs[1]
+
+fsdec = ort.InferenceSession(MODEL_PATH+"_export_t2s_fsdec.onnx")
+sdec = ort.InferenceSession(MODEL_PATH+"_export_t2s_sdec.onnx")
+
+# for i in tqdm(range(10000)):
+[y, k, v, y_emb, x_example] = fsdec.run(None, {
+    "x": x,
+    "prompts": prompts
+})
+
+early_stop_num = -1
+prefix_len = prompts.shape[1]
+
+stop = False
+for idx in tqdm(range(1, 1500)):
+    # [1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
+    [y, k, v, y_emb, logits, samples] = sdec.run(None, {
+        "iy": y,
+        "ik": k,
+        "iv": v,
+        "iy_emb": y_emb,
+        "ix_example": x_example
+    })
+    if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+        stop = True
+    if np.argmax(logits, axis=-1)[0] == 1024 or samples[0, 0] == 1024:
+        stop = True
+    if stop:
+        break
+y[0, -1] = 0
+
+
+pred_semantic = np.expand_dims(y[:, -idx:], axis=0)
+# Read and resample reference audio
+waveform, sample_rate = torchaudio.load("playground/ref/audio.wav")
+if sample_rate != 32000:
+    resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=32000)
+    waveform = resampler(waveform)
+ref_audio = waveform.numpy().astype(np.float32)
+vtis = ort.InferenceSession(MODEL_PATH+"_export_vits.onnx")
+
+[audio] = vtis.run(None, {
+    "text_seq": input_phones,
+    "pred_semantic": pred_semantic,
+    "ref_audio": ref_audio
+})
+print(audio.shape, audio.dtype, audio.min(), audio.max())
+
+audio_postprocess([audio])