Use clap to achieve prompt controlled generation (#223)

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* Update models.py

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* Create compress_model.py

* 重新提交，更新Gradio推理UI (#193)

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* 更新 train_ms.py

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* Update preprocess_text.py

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* fix (#211)

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* add cluster center save

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* Update config.py

* Update default_config.yml

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* Update config.py

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* Update train_ms.py

* Add CLAP

* Fix data loader

* Fix infer.py

* Fix webui.py

* Add prompt template

* Update clap_gen.py

* Fix wrong environ value

* Add g for dur disc

* Update clap_gen.py

* Fix multilang generation

* Update config.json

* Prompt mode

* Improve slice segments performance

* Add preprocess webui

* Update webui_preprocess.py

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* Update config.py

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* Update config.py

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* Delete emo_gen.py

* Delete get_emo.py

* Delete emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim directory

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* Split val per lang

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* Update defalut_config.yml

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resample.py

@@ -10,11 +10,11 @@ from config import config
 
 
 def process(item):
-    spkdir, wav_name, args = item
-    wav_path = os.path.join(args.in_dir, spkdir, wav_name)
+    wav_name, args = item
+    wav_path = os.path.join(args.in_dir, wav_name)
     if os.path.exists(wav_path) and wav_path.lower().endswith(".wav"):
         wav, sr = librosa.load(wav_path, sr=args.sr)
-        soundfile.write(os.path.join(args.out_dir, spkdir, wav_name), wav, sr)
+        soundfile.write(os.path.join(args.out_dir, wav_name), wav, sr)
 
 
 if __name__ == "__main__":
@@ -54,15 +54,11 @@ if __name__ == "__main__":
     tasks = []
 
     for dirpath, _, filenames in os.walk(args.in_dir):
-        # 子级目录
-        spk_dir = os.path.relpath(dirpath, args.in_dir)
-        spk_dir_out = os.path.join(args.out_dir, spk_dir)
-        if not os.path.isdir(spk_dir_out):
-            os.makedirs(spk_dir_out, exist_ok=True)
+        if not os.path.isdir(args.out_dir):
+            os.makedirs(args.out_dir, exist_ok=True)
         for filename in filenames:
             if filename.lower().endswith(".wav"):
-                twople = (spk_dir, filename, args)
-                tasks.append(twople)
+                tasks.append((filename, args))
 
     for _ in tqdm(
         pool.imap_unordered(process, tasks),