Change the utils.download_emo_models (#199)

* Change the utils.download_emo_models

Change utils.download_emo_models(config.mirror, model_name, REPO_ID) to utils.download_emo_models(config.mirror, REPO_ID, model_name)

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Author: Thymustw

emo_gen.py

@@ -1,162 +1,163 @@
-import argparse
-import os
-from pathlib import Path
-
-import librosa
-import numpy as np
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader, Dataset
-from tqdm import tqdm
-from transformers import Wav2Vec2Processor
-from transformers.models.wav2vec2.modeling_wav2vec2 import (
-    Wav2Vec2Model,
-    Wav2Vec2PreTrainedModel,
-)
-
-import utils
-from config import config
-
-
-class RegressionHead(nn.Module):
-    r"""Classification head."""
-
-    def __init__(self, config):
-        super().__init__()
-
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.dropout = nn.Dropout(config.final_dropout)
-        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
-
-    def forward(self, features, **kwargs):
-        x = features
-        x = self.dropout(x)
-        x = self.dense(x)
-        x = torch.tanh(x)
-        x = self.dropout(x)
-        x = self.out_proj(x)
-
-        return x
-
-
-class EmotionModel(Wav2Vec2PreTrainedModel):
-    r"""Speech emotion classifier."""
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.config = config
-        self.wav2vec2 = Wav2Vec2Model(config)
-        self.classifier = RegressionHead(config)
-        self.init_weights()
-
-    def forward(
-        self,
-        input_values,
-    ):
-        outputs = self.wav2vec2(input_values)
-        hidden_states = outputs[0]
-        hidden_states = torch.mean(hidden_states, dim=1)
-        logits = self.classifier(hidden_states)
-
-        return hidden_states, logits
-
-
-class AudioDataset(Dataset):
-    def __init__(self, list_of_wav_files, sr, processor):
-        self.list_of_wav_files = list_of_wav_files
-        self.processor = processor
-        self.sr = sr
-
-    def __len__(self):
-        return len(self.list_of_wav_files)
-
-    def __getitem__(self, idx):
-        wav_file = self.list_of_wav_files[idx]
-        audio_data, _ = librosa.load(wav_file, sr=self.sr)
-        processed_data = self.processor(audio_data, sampling_rate=self.sr)[
-            "input_values"
-        ][0]
-        return torch.from_numpy(processed_data)
-
-
-def process_func(
-    x: np.ndarray,
-    sampling_rate: int,
-    model: EmotionModel,
-    processor: Wav2Vec2Processor,
-    device: str,
-    embeddings: bool = False,
-) -> np.ndarray:
-    r"""Predict emotions or extract embeddings from raw audio signal."""
-    model = model.to(device)
-    y = processor(x, sampling_rate=sampling_rate)
-    y = y["input_values"][0]
-    y = torch.from_numpy(y).unsqueeze(0).to(device)
-
-    # run through model
-    with torch.no_grad():
-        y = model(y)[0 if embeddings else 1]
-
-    # convert to numpy
-    y = y.detach().cpu().numpy()
-
-    return y
-
-
-def get_emo(path):
-    wav, sr = librosa.load(path, 16000)
-    device = config.bert_gen_config.device
-    return process_func(
-        np.expand_dims(wav, 0).astype(np.float64),
-        sr,
-        model,
-        processor,
-        device,
-        embeddings=True,
-    ).squeeze(0)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-c", "--config", type=str, default=config.bert_gen_config.config_path
-    )
-    parser.add_argument(
-        "--num_processes", type=int, default=config.bert_gen_config.num_processes
-    )
-    args, _ = parser.parse_known_args()
-    config_path = args.config
-    hps = utils.get_hparams_from_file(config_path)
-
-    device = config.bert_gen_config.device
-
-    model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
-    REPO_ID = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim"
-    if not Path(model_name).joinpath("pytorch_model.bin").exists():
-        utils.download_emo_models(config.mirror, model_name, REPO_ID)
-
-    processor = Wav2Vec2Processor.from_pretrained(model_name)
-    model = EmotionModel.from_pretrained(model_name).to(device)
-
-    lines = []
-    with open(hps.data.training_files, encoding="utf-8") as f:
-        lines.extend(f.readlines())
-
-    with open(hps.data.validation_files, encoding="utf-8") as f:
-        lines.extend(f.readlines())
-
-    wavnames = [line.split("|")[0] for line in lines]
-    dataset = AudioDataset(wavnames, 16000, processor)
-    data_loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=16)
-
-    with torch.no_grad():
-        for i, data in tqdm(enumerate(data_loader), total=len(data_loader)):
-            wavname = wavnames[i]
-            emo_path = wavname.replace(".wav", ".emo.npy")
-            if os.path.exists(emo_path):
-                continue
-            emb = model(data.to(device))[0].detach().cpu().numpy()
-            np.save(emo_path, emb)
-
+import argparse
+import os
+from pathlib import Path
+
+import librosa
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
+from transformers import Wav2Vec2Processor
+from transformers.models.wav2vec2.modeling_wav2vec2 import (
+    Wav2Vec2Model,
+    Wav2Vec2PreTrainedModel,
+)
+
+import utils
+from config import config
+
+
+class RegressionHead(nn.Module):
+    r"""Classification head."""
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.final_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+
+        return x
+
+
+class EmotionModel(Wav2Vec2PreTrainedModel):
+    r"""Speech emotion classifier."""
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.config = config
+        self.wav2vec2 = Wav2Vec2Model(config)
+        self.classifier = RegressionHead(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_values,
+    ):
+        outputs = self.wav2vec2(input_values)
+        hidden_states = outputs[0]
+        hidden_states = torch.mean(hidden_states, dim=1)
+        logits = self.classifier(hidden_states)
+
+        return hidden_states, logits
+
+
+class AudioDataset(Dataset):
+    def __init__(self, list_of_wav_files, sr, processor):
+        self.list_of_wav_files = list_of_wav_files
+        self.processor = processor
+        self.sr = sr
+
+    def __len__(self):
+        return len(self.list_of_wav_files)
+
+    def __getitem__(self, idx):
+        wav_file = self.list_of_wav_files[idx]
+        audio_data, _ = librosa.load(wav_file, sr=self.sr)
+        processed_data = self.processor(audio_data, sampling_rate=self.sr)[
+            "input_values"
+        ][0]
+        return torch.from_numpy(processed_data)
+
+
+def process_func(
+    x: np.ndarray,
+    sampling_rate: int,
+    model: EmotionModel,
+    processor: Wav2Vec2Processor,
+    device: str,
+    embeddings: bool = False,
+) -> np.ndarray:
+    r"""Predict emotions or extract embeddings from raw audio signal."""
+    model = model.to(device)
+    y = processor(x, sampling_rate=sampling_rate)
+    y = y["input_values"][0]
+    y = torch.from_numpy(y).unsqueeze(0).to(device)
+
+    # run through model
+    with torch.no_grad():
+        y = model(y)[0 if embeddings else 1]
+
+    # convert to numpy
+    y = y.detach().cpu().numpy()
+
+    return y
+
+
+def get_emo(path):
+    wav, sr = librosa.load(path, 16000)
+    device = config.bert_gen_config.device
+    return process_func(
+        np.expand_dims(wav, 0).astype(np.float),
+        sr,
+        model,
+        processor,
+        device,
+        embeddings=True,
+    ).squeeze(0)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c", "--config", type=str, default=config.bert_gen_config.config_path
+    )
+    parser.add_argument(
+        "--num_processes", type=int, default=config.bert_gen_config.num_processes
+    )
+    args, _ = parser.parse_known_args()
+    config_path = args.config
+    hps = utils.get_hparams_from_file(config_path)
+
+    device = config.bert_gen_config.device
+
+    model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
+    REPO_ID = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim"
+    if not Path(model_name).joinpath("pytorch_model.bin").exists():
+        utils.download_emo_models(config.mirror, REPO_ID, model_name)
+
+    processor = Wav2Vec2Processor.from_pretrained(model_name)
+    model = EmotionModel.from_pretrained(model_name).to(device)
+
+    lines = []
+    with open(hps.data.training_files, encoding="utf-8") as f:
+        lines.extend(f.readlines())
+
+    with open(hps.data.validation_files, encoding="utf-8") as f:
+        lines.extend(f.readlines())
+
+    wavnames = [line.split("|")[0] for line in lines]
+    dataset = AudioDataset(wavnames, 16000, processor)
+    data_loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=16)
+
+    with torch.no_grad():
+        for i, data in tqdm(enumerate(data_loader), total=len(data_loader)):
+            wavname = wavnames[i]
+            emo_path = wavname.replace(".wav", ".emo.npy")
+            if os.path.exists(emo_path):
+                continue
+            emb = model(data.to(device))[0].detach().cpu().numpy()
+            np.save(emo_path, emb)
+
     print("Emo vec 生成完毕!")