Update predict.py

predict.py

@@ -1,62 +1,59 @@
-import os
-import json
-import random
-import librosa
-import numpy as np
-import gradio as gr
-from typing import Any, List, Dict, Tuple
-
-from utils import meow_stretch, get_word_lengths
-from config import config, BaseConfig
-
-COUNTER = 0
-
-''' Gradio Input/Output Configurations '''
-inputs: str = 'text'
-outputs: gr.Audio = gr.Audio()
-
-def load_meows(cfg: BaseConfig) -> List[Dict[str, Any]]:
-
-    meow_dir = os.path.dirname(cfg.manifest_path)
-
-    with open(cfg.manifest_path, mode='r') as fr:
-        lines = fr.readlines()
-
-    items = []
-    for line in lines:
-        item = json.loads(line)
-        item['audio'], item['rate'] = librosa.load(os.path.join(meow_dir, item['audio_filepath']), sr=None)
-        items.append(item)
-
-    return items
-
-def extract_meows_weights(items: List[Dict[str, Any]]) -> Tuple[List[np.ndarray], List[float]]:
-    meows = [item['audio'] for item in items]
-    weights = [item['weight'] for item in items]
-    return meows, weights
-
-''' Load meows '''
-meow_items = load_meows(config)
-meows, weights = extract_meows_weights(meow_items)
-
-def predict(text: str) -> str:
-
-    COUNTER += 1
-    print(f'Number of calls:', COUNTER)
-
-    word_lengths = get_word_lengths(text)
-    selected_meows = random.choices(meows, weights=weights, k=len(word_lengths))
-    transformed_meows = [
-        meow_stretch(
-            meow, wl,
-            init_factor=config.init_factor,
-            add_factor=config.add_factor,
-            power_factor=config.power_factor
-        ) for meow, wl in zip(selected_meows, word_lengths)
-    ]
-
-    result_meows = np.concatenate(transformed_meows, axis=0)
-
-    return (config.sample_rate, result_meows)
-
-
+import os
+import json
+import random
+import librosa
+import numpy as np
+import gradio as gr
+from typing import Any, List, Dict, Tuple
+
+from utils import meow_stretch, get_word_lengths
+from config import config, BaseConfig
+
+COUNTER = 0
+
+''' Gradio Input/Output Configurations '''
+inputs: str = 'text'
+outputs: gr.Audio = gr.Audio()
+
+def load_meows(cfg: BaseConfig) -> List[Dict[str, Any]]:
+
+    meow_dir = os.path.dirname(cfg.manifest_path)
+
+    with open(cfg.manifest_path, mode='r') as fr:
+        lines = fr.readlines()
+
+    items = []
+    for line in lines:
+        item = json.loads(line)
+        item['audio'], item['rate'] = librosa.load(os.path.join(meow_dir, item['audio_filepath']), sr=None)
+        items.append(item)
+
+    return items
+
+def extract_meows_weights(items: List[Dict[str, Any]]) -> Tuple[List[np.ndarray], List[float]]:
+    meows = [item['audio'] for item in items]
+    weights = [item['weight'] for item in items]
+    return meows, weights
+
+''' Load meows '''
+meow_items = load_meows(config)
+meows, weights = extract_meows_weights(meow_items)
+
+def predict(text: str) -> str:
+
+    word_lengths = get_word_lengths(text)
+    selected_meows = random.choices(meows, weights=weights, k=len(word_lengths))
+    transformed_meows = [
+        meow_stretch(
+            meow, wl,
+            init_factor=config.init_factor,
+            add_factor=config.add_factor,
+            power_factor=config.power_factor
+        ) for meow, wl in zip(selected_meows, word_lengths)
+    ]
+
+    result_meows = np.concatenate(transformed_meows, axis=0)
+
+    return (config.sample_rate, result_meows)
+
+