Merge pull request #365 from svc-develop-team/4.1-Stable

To Latest
2023-08-02 00:43:07 +08:00 · 2023-08-02 00:43:07 +08:00 · 39b0befef5
parent c9d81428fe 27f644d435
commit 39b0befef5
5 changed files with 51 additions and 35 deletions
--- a/modules/F0Predictor/fcpe/model.py
+++ b/modules/F0Predictor/fcpe/model.py
@ -136,7 +136,7 @@ class FCPE(nn.Module):
        B, N, _ = y.size()
        ci = self.cent_table[None, None, :].expand(B, N, -1)
        confident, max_index = torch.max(y, dim=-1, keepdim=True)
-        local_argmax_index = torch.arange(0,8).to(max_index.device) + (max_index - 4)
+        local_argmax_index = torch.arange(0,9).to(max_index.device) + (max_index - 4)
        local_argmax_index[local_argmax_index<0] = 0
        local_argmax_index[local_argmax_index>=self.n_out] = self.n_out - 1
        ci_l = torch.gather(ci,-1,local_argmax_index)
--- a/preprocess_flist_config.py
+++ b/preprocess_flist_config.py
@ -13,14 +13,17 @@ import diffusion.logger.utils as du
 pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')

 def get_wav_duration(file_path):
-    with wave.open(file_path, 'rb') as wav_file:
-        # 获取音频帧数
-        n_frames = wav_file.getnframes()
-        # 获取采样率
-        framerate = wav_file.getframerate()
-        # 计算时长（秒）
-        duration = n_frames / float(framerate)
-    return duration
+    try:
+        with wave.open(file_path, 'rb') as wav_file:
+            # 获取音频帧数
+            n_frames = wav_file.getnframes()
+            # 获取采样率
+            framerate = wav_file.getframerate()
+            # 计算时长（秒）
+            return n_frames / float(framerate)
+    except Exception as e:
+        logger.error(f"Reading {file_path}")
+        raise e

 if __name__ == "__main__":
    parser = argparse.ArgumentParser()
@ -42,32 +45,39 @@ if __name__ == "__main__":
    for speaker in tqdm(os.listdir(args.source_dir)):
        spk_dict[speaker] = spk_id
        spk_id += 1
-        wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
-        new_wavs = []
-        for file in wavs:
-            if not file.endswith("wav"):
+        wavs = []
+
+        for file_name in os.listdir(os.path.join(args.source_dir, speaker)):
+            if not file_name.endswith("wav"):
                continue
-            if not pattern.match(file):
-                logger.warning(f"文件名{file}中包含非字母数字下划线，可能会导致错误。（也可能不会）")
-            if get_wav_duration(file) < 0.3:
-                logger.info("Skip too short audio:" + file)
+            if file_name.startswith("."):
                continue
-            new_wavs.append(file)
-        wavs = new_wavs
+
+            file_path = "/".join([args.source_dir, speaker, file_name])
+
+            if not pattern.match(file_name):
+                logger.warning("Detected non-ASCII file name: " + file_path)
+
+            if get_wav_duration(file_path) < 0.3:
+                logger.info("Skip too short audio: " + file_path)
+                continue
+
+            wavs.append(file_path)
+
        shuffle(wavs)
        train += wavs[2:]
        val += wavs[:2]

    shuffle(train)
    shuffle(val)
-            
-    logger.info("Writing" + args.train_list)
+
+    logger.info("Writing " + args.train_list)
    with open(args.train_list, "w") as f:
        for fname in tqdm(train):
            wavpath = fname
            f.write(wavpath + "\n")
-        
-    logger.info("Writing" + args.val_list)
+
+    logger.info("Writing " + args.val_list)
    with open(args.val_list, "w") as f:
        for fname in tqdm(val):
            wavpath = fname
--- a/preprocess_hubert_f0.py
+++ b/preprocess_hubert_f0.py
@ -113,7 +113,7 @@ def process_batch(file_chunk, f0p, diff=False, mel_extractor=None, device="cpu")
    logger.info(f"Rank {rank} uses device {device}")
    hmodel = utils.get_speech_encoder(speech_encoder, device=device)
    logger.info(f"Loaded speech encoder for rank {rank}")
-    for filename in tqdm(file_chunk):
+    for filename in tqdm(file_chunk, position = rank):
        process_one(filename, hmodel, f0p, device, diff, mel_extractor)

 def parallel_process(filenames, num_processes, f0p, diff, mel_extractor, device):
@ -124,7 +124,7 @@ def parallel_process(filenames, num_processes, f0p, diff, mel_extractor, device)
            end = int((i + 1) * len(filenames) / num_processes)
            file_chunk = filenames[start:end]
            tasks.append(executor.submit(process_batch, file_chunk, f0p, diff, mel_extractor, device=device))
-        for task in tqdm(tasks):
+        for task in tqdm(tasks, position = 0):
            task.result()

 if __name__ == "__main__":
@ -149,10 +149,10 @@ if __name__ == "__main__":
        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    print(speech_encoder)
-    logger.info("Using device: ", device)
+    logger.info("Using device: " + str(device))
    logger.info("Using SpeechEncoder: " + speech_encoder)
    logger.info("Using extractor: " + f0p)
-    logger.info("Using diff Mode: " + str( args.use_diff))
+    logger.info("Using diff Mode: " + str(args.use_diff))

    if args.use_diff:
        print("use_diff")
--- a/pretrain/init.py
+++ b/pretrain/init.py
--- a/sovits4_for_colab.ipynb
+++ b/sovits4_for_colab.ipynb
@ -78,10 +78,9 @@
    "#@markdown\n",
    "\n",
    "!git clone https://github.com/svc-develop-team/so-vits-svc -b 4.1-Stable\n",
-    "%pip uninstall -y torchdata torchtext\n",
-    "%pip install --upgrade pip setuptools numpy numba\n",
-    "%pip install pyworld praat-parselmouth fairseq tensorboardX torchcrepe librosa==0.9.1 pyyaml pynvml pyloudnorm faiss-gpu\n",
-    "%pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118\n",
+    "%cd /content/so-vits-svc\n",
+    "%pip install --upgrade pip setuptools\n",
+    "%pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118\n",
    "exit()"
   ]
  },
@ -163,8 +162,8 @@
    "#@markdown Although the pretrained model generally does not cause any copyright problems, please pay attention to it. For example, ask the author in advance, or the author has indicated the feasible use in the description clearly.\n",
    "\n",
    "download_pretrained_model = True #@param {type:\"boolean\"}\n",
-    "D_0_URL = \"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_D_320000.pth\" #@param [\"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_D_320000.pth\", \"https://huggingface.co/1asbgdh/sovits4.0-volemb-vec768/resolve/main/clean_D_320000.pth\"] {allow-input: true}\n",
-    "G_0_URL = \"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_G_320000.pth\" #@param [\"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_G_320000.pth\", \"https://huggingface.co/1asbgdh/sovits4.0-volemb-vec768/resolve/main/clean_G_320000.pth\"] {allow-input: true}\n",
+    "D_0_URL = \"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_D_320000.pth\" #@param [\"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_D_320000.pth\", \"https://huggingface.co/1asbgdh/sovits4.0-volemb-vec768/resolve/main/clean_D_320000.pth\", \"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/vol_emb/clean_D_320000.pth\"] {allow-input: true}\n",
+    "G_0_URL = \"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_G_320000.pth\" #@param [\"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_G_320000.pth\", \"https://huggingface.co/1asbgdh/sovits4.0-volemb-vec768/resolve/main/clean_G_320000.pth\", \"https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/vol_emb/clean_G_320000.pth\"] {allow-input: true}\n",
    "\n",
    "download_pretrained_diffusion_model = True #@param {type:\"boolean\"}\n",
    "diff_model_URL = \"https://huggingface.co/datasets/ms903/Diff-SVC-refactor-pre-trained-model/resolve/main/fix_pitch_add_vctk_600k/model_0.pt\" #@param {type:\"string\"}\n",
@ -317,13 +316,17 @@
    "#@markdown\n",
    "%cd /content/so-vits-svc\n",
    "\n",
-    "f0_predictor = \"crepe\" #@param [\"crepe\", \"pm\", \"dio\", \"harvest\", \"rmvpe\"]\n",
+    "f0_predictor = \"crepe\" #@param [\"crepe\", \"pm\", \"dio\", \"harvest\", \"rmvpe\", \"fcpe\"]\n",
    "use_diff = True #@param {type:\"boolean\"}\n",
    "\n",
    "import os\n",
    "if f0_predictor == \"rmvpe\" and not os.path.exists(\"./pretrain/rmvpe.pt\"):\n",
    "  !curl -L https://huggingface.co/datasets/ylzz1997/rmvpe_pretrain_model/resolve/main/rmvpe.pt -o pretrain/rmvpe.pt\n",
    "\n",
+    "if f0_predictor == \"fcpe\" and not os.path.exists(\"./pretrain/fcpe.pt\"):\n",
+    "  !curl -L https://huggingface.co/datasets/ylzz1997/rmvpe_pretrain_model/resolve/main/fcpe.pt -o pretrain/fcpe.pt\n",
+    "\n",
+    "\n",
    "diff_param = \"\"\n",
    "if use_diff:\n",
    "  diff_param = \"--use_diff\"\n",
@ -624,7 +627,7 @@
    "if auto_predict_f0:\n",
    "  apf = \" -a \"\n",
    "\n",
-    "f0_predictor = \"crepe\" #@param [\"crepe\", \"pm\", \"dio\", \"harvest\", \"rmvpe\"]\n",
+    "f0_predictor = \"crepe\" #@param [\"crepe\", \"pm\", \"dio\", \"harvest\", \"rmvpe\", \"fcpe\"]\n",
    "\n",
    "enhance = False  #@param {type:\"boolean\"}\n",
    "ehc = \"\"\n",
@ -644,6 +647,9 @@
    "if f0_predictor == \"rmvpe\" and not os.path.exists(\"./pretrain/rmvpe.pt\"):\n",
    "  !curl -L https://huggingface.co/datasets/ylzz1997/rmvpe_pretrain_model/resolve/main/rmvpe.pt -o pretrain/rmvpe.pt\n",
    "\n",
+    "if f0_predictor == \"fcpe\" and not os.path.exists(\"./pretrain/fcpe.pt\"):\n",
+    "  !curl -L https://huggingface.co/datasets/ylzz1997/rmvpe_pretrain_model/resolve/main/fcpe.pt -o pretrain/fcpe.pt\n",
+    "\n",
    "if not os.path.exists(output):\n",
    "  !curl -L {url} -o {output}\n",
    "\n",