Update app.py

app.py

@@ -4,20 +4,27 @@ import gradio as gr
 import os
 import random
 import torch
+import openai
 
+# Check if running in a duplicate space
 IS_DUPLICATE = not os.getenv('SPACE_ID', '').startswith('hexgrad/')
 CHAR_LIMIT = None if IS_DUPLICATE else 5000
 
+# Check if CUDA is available
 CUDA_AVAILABLE = torch.cuda.is_available()
+
+# Load the models (GPU and CPU versions)
 models = {gpu: KModel().to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
 pipelines = {lang_code: KPipeline(lang_code=lang_code, model=False) for lang_code in 'abefhijpz'}
 pipelines['a'].g2p.lexicon.golds['kokoro'] = 'kˈOkəɹO'
 pipelines['b'].g2p.lexicon.golds['kokoro'] = 'kˈQkəɹQ'
 
+# GPU function to generate audio
 @spaces.GPU(duration=10)
 def forward_gpu(ps, ref_s, speed):
     return models[True](ps, ref_s, speed)
 
+# Function to generate first output
 def generate_first(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
     text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT]
     pipeline = pipelines[voice[0]]
@@ -40,36 +47,12 @@ def generate_first(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
         return (24000, audio.numpy()), ps
     return None, ''
 
-# Arena API
-def predict(text, voice='af_heart', speed=1):
-    return generate_first(text, voice, speed, use_gpu=False)[0]
-
+# Function to tokenize first
 def tokenize_first(text, voice='af_heart'):
-    # Split the input text into words and return as a list of words (fix applied here)
     words = text.split()  # This splits the text into words based on spaces
     return words  # Return a list of words
 
-def generate_all(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
-    text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT]
-    pipeline = pipelines[voice[0]]
-    pack = pipeline.load_voice(voice)
-    use_gpu = use_gpu and CUDA_AVAILABLE
-    for _, ps, _ in pipeline(text, voice, speed):
-        ref_s = pack[len(ps)-1]
-        try:
-            if use_gpu:
-                audio = forward_gpu(ps, ref_s, speed)
-            else:
-                audio = models[False](ps, ref_s, speed)
-        except gr.exceptions.Error as e:
-            if use_gpu:
-                gr.Warning(str(e))
-                gr.Info('Switching to CPU')
-                audio = models[False](ps, ref_s, speed)
-            else:
-                raise gr.Error(e)
-        yield 24000, audio.numpy()
-
+# Function to get random text for the "Random Text" button
 random_texts = {}
 for lang in ['en']:
     with open(f'{lang}.txt', 'r') as r:
@@ -79,81 +62,104 @@ def get_random_text(voice):
     lang = dict(a='en', b='en')[voice[0]]
     return random.choice(random_texts[lang])
 
+# OpenAI GPT-4 translation function
+def translate_to_english(text, model="gpt-4"):
+    try:
+        response = openai.Completion.create(
+            model=model,
+            prompt=f"Translate the following text to English:\n\n{text}",
+            temperature=0.5,
+            max_tokens=500,
+        )
+        return response.choices[0].text.strip()
+    except Exception as e:
+        return str(e)
+
+# Function to handle generation for translated text
+def translate_and_generate(text, voice, speed):
+    translated_text = translate_to_english(text)
+    audio, tokens = generate_first(translated_text, voice, speed, use_gpu=CUDA_AVAILABLE)
+    return audio, tokens, translated_text
+
+# Predefined voices for the dropdown menu
 CHOICES = {
-'🇺🇸 🚺 Heart ❤️': 'af_heart',
-'🇺🇸 🚺 Bella 🔥': 'af_bella',
-'🇺🇸 🚺 Nicole 🎧': 'af_nicole',
-'🇺🇸 🚺 Aoede': 'af_aoede',
-'🇺🇸 🚺 Kore': 'af_kore',
-'🇺🇸 🚺 Sarah': 'af_sarah',
-'🇺🇸 🚺 Nova': 'af_nova',
-'🇺🇸 🚺 Sky': 'af_sky',
-'🇺🇸 🚺 Alloy': 'af_alloy',
-'🇺🇸 🚺 Jessica': 'af_jessica',
-'🇺🇸 🚺 River': 'af_river',
-    
-'🇺🇸 🚹 Michael': 'am_michael',
-'🇺🇸 🚹 Fenrir': 'am_fenrir',
-'🇺🇸 🚹 Puck': 'am_puck',
-'🇺🇸 🚹 Echo': 'am_echo',
-'🇺🇸 🚹 Eric': 'am_eric',
-'🇺🇸 🚹 Liam': 'am_liam',
-'🇺🇸 🚹 Onyx': 'am_onyx',
-'🇺🇸 🚹 Santa': 'am_santa',
-'🇺🇸 🚹 Adam': 'am_adam',
-    
-'🇬🇧 🚺 Emma': 'bf_emma',
-'🇬🇧 🚺 Isabella': 'bf_isabella',
-'🇬🇧 🚺 Alice': 'bf_alice',
-'🇬🇧 🚺 Lily': 'bf_lily',
-    
-'🇬🇧 🚹 George': 'bm_george',
-'🇬🇧 🚹 Fable': 'bm_fable',
-'🇬🇧 🚹 Lewis': 'bm_lewis',
-'🇬🇧 🚹 Daniel': 'bm_daniel',
-    
-'🇪🇸 🚺 Dora': 'ef_dora',
-    
-'🇪🇸 🚹 Alex': 'em_alex',
-'🇪🇸 🚹 Santa': 'em_santa',
-    
-'🇫🇷 🚺 Siwis': 'ff_siwis',
-    
-'🇮🇳 🚹 Alpha': 'hf_alpha',
-'🇮🇳 🚹 Beta': 'hf_beta',
-    
-'🇮🇳 🚹 Omega': 'hm_omega',
-'🇮🇳 🚹 Psi': 'hm_psi',
-    
-'🇮🇹 🚺 Sara': 'if_sara',
-    
-'🇮🇹 🚺 Nicola': 'im_nicola',
-    
-'🇯🇵 🚹 Alpha': 'jf_alpha',
-'🇯🇵 🚹 Gongitsune': 'jf_gongitsune',
-'🇯🇵 🚹 Nezumi': 'jf_nezumi',
-'🇯🇵 🚹 Tebukuro': 'jf_tebukuro',
-    
-'🇯🇵 🚹 Kumo': 'jm_kumo',
-    
-'🇧🇷 🚺 Dora': 'pf_dora',
-    
-'🇧🇷 🚹 Alex': 'pm_alex',
-'🇧🇷 🚹 Santa': 'pm_santa',
-    
-'🇨🇳 🚺 Xiaobei': 'zf_xiaobei',
-'🇨🇳 🚺 Xiaoni': 'zf_xiaoni',
-'🇨🇳 🚺 Xiaoxiao': 'zf_xiaoxiao',
-'🇨🇳 🚺 Xiaoyi': 'zf_xiaoyi',
-
-'🇨🇳 🚹 Yunjian': 'zm_yunjian',
-'🇨🇳 🚹 Yunxi': 'zm_yunxi',
-'🇨🇳 🚹 Yunxia': 'zm_yunxia',
-'🇨🇳 🚹 Yunyang': 'zm_yunyang',
+    '🇺🇸 🚺 Heart ❤️': 'af_heart',
+    '🇺🇸 🚺 Bella 🔥': 'af_bella',
+    '🇺🇸 🚺 Nicole 🎧': 'af_nicole',
+    '🇺🇸 🚺 Aoede': 'af_aoede',
+    '🇺🇸 🚺 Kore': 'af_kore',
+    '🇺🇸 🚺 Sarah': 'af_sarah',
+    '🇺🇸 🚺 Nova': 'af_nova',
+    '🇺🇸 🚺 Sky': 'af_sky',
+    '🇺🇸 🚺 Alloy': 'af_alloy',
+    '🇺🇸 🚺 Jessica': 'af_jessica',
+    '🇺🇸 🚺 River': 'af_river',
+    
+    '🇺🇸 🚹 Michael': 'am_michael',
+    '🇺🇸 🚹 Fenrir': 'am_fenrir',
+    '🇺🇸 🚹 Puck': 'am_puck',
+    '🇺🇸 🚹 Echo': 'am_echo',
+    '🇺🇸 🚹 Eric': 'am_eric',
+    '🇺🇸 🚹 Liam': 'am_liam',
+    '🇺🇸 🚹 Onyx': 'am_onyx',
+    '🇺🇸 🚹 Santa': 'am_santa',
+    '🇺🇸 🚹 Adam': 'am_adam',
+    
+    '🇬🇧 🚺 Emma': 'bf_emma',
+    '🇬🇧 🚺 Isabella': 'bf_isabella',
+    '🇬🇧 🚺 Alice': 'bf_alice',
+    '🇬🇧 🚺 Lily': 'bf_lily',
+    
+    '🇬🇧 🚹 George': 'bm_george',
+    '🇬🇧 🚹 Fable': 'bm_fable',
+    '🇬🇧 🚹 Lewis': 'bm_lewis',
+    '🇬🇧 🚹 Daniel': 'bm_daniel',
+    
+    '🇪🇸 🚺 Dora': 'ef_dora',
+    
+    '🇪🇸 🚹 Alex': 'em_alex',
+    '🇪🇸 🚹 Santa': 'em_santa',
+    
+    '🇫🇷 🚺 Siwis': 'ff_siwis',
+    
+    '🇮🇳 🚹 Alpha': 'hf_alpha',
+    '🇮🇳 🚹 Beta': 'hf_beta',
+    
+    '🇮🇳 🚹 Omega': 'hm_omega',
+    '🇮🇳 🚹 Psi': 'hm_psi',
+    
+    '🇮🇹 🚺 Sara': 'if_sara',
+    
+    '🇮🇹 🚺 Nicola': 'im_nicola',
+    
+    '🇯🇵 🚹 Alpha': 'jf_alpha',
+    '🇯🇵 🚹 Gongitsune': 'jf_gongitsune',
+    '🇯🇵 🚹 Nezumi': 'jf_nezumi',
+    '🇯🇵 🚹 Tebukuro': 'jf_tebukuro',
+    
+    '🇯🇵 🚹 Kumo': 'jm_kumo',
+    
+    '🇧🇷 🚺 Dora': 'pf_dora',
+    
+    '🇧🇷 🚹 Alex': 'pm_alex',
+    '🇧🇷 🚹 Santa': 'pm_santa',
+    
+    '🇨🇳 🚺 Xiaobei': 'zf_xiaobei',
+    '🇨🇳 🚺 Xiaoni': 'zf_xiaoni',
+    '🇨🇳 🚺 Xiaoxiao': 'zf_xiaoxiao',
+    '🇨🇳 🚺 Xiaoyi': 'zf_xiaoyi',
+
+    '🇨🇳 🚹 Yunjian': 'zm_yunjian',
+    '🇨🇳 🚹 Yunxi': 'zm_yunxi',
+    '🇨🇳 🚹 Yunxia': 'zm_yunxia',
+    '🇨🇳 🚹 Yunyang': 'zm_yunyang',
 }
+
+# Load voices
 for v in CHOICES.values():
     pipelines[v[0]].load_voice(v)
 
+# Build the interface
 with gr.Blocks() as generate_tab:
     out_audio = gr.Audio(label='Output Audio', interactive=False, streaming=False, autoplay=True)
     generate_btn = gr.Button('Generate', variant='primary')
@@ -162,17 +168,21 @@ with gr.Blocks() as generate_tab:
         tokenize_btn = gr.Button('Tokenize', variant='secondary')
         predict_btn = gr.Button('Predict', variant='secondary', visible=False)
 
-BANNER_TEXT = '''
-[***Kokoro*** **is an open-weight TTS model with 82 million parameters.**](https://huggingface.co/hexgrad/Kokoro-82M)
-As of January 31st, 2025, Kokoro was the most-liked [**TTS model**](https://huggingface.co/models?pipeline_tag=text-to-speech&sort=likes) and the most-liked [**TTS space**](https://huggingface.co/spaces?sort=likes&search=tts) on Hugging Face.
-This demo only showcases English, but you can directly use the model to access other languages.
-'''
+# Translator Tab
+with gr.Blocks() as translator_tab:
+    trans_out_audio = gr.Audio(label='Translated Audio Output', interactive=False, streaming=False, autoplay=True)
+    trans_out_tokens = gr.Textbox(interactive=False, show_label=False, info='Tokens used to generate the translated audio')
+    translate_btn = gr.Button('Translate & Generate Audio', variant='primary')
+    
+    translate_btn.click(fn=translate_and_generate, inputs=[text, voice, speed], outputs=[trans_out_audio, trans_out_tokens, text], api_name=None)
 
-API_OPEN = os.getenv('SPACE_ID') != 'hexgrad/Kokoro-TTS'
-API_NAME = None if API_OPEN else False
+# Main Interface
 with gr.Blocks() as app:
     with gr.Row():
-        gr.Markdown(BANNER_TEXT, container=True)
+        gr.Markdown('''[***Kokoro*** **is an open-weight TTS model with 82 million parameters.**](https://huggingface.co/hexgrad/Kokoro-82M)
+        As of January 31st, 2025, Kokoro was the most-liked [**TTS model**](https://huggingface.co/models?pipeline_tag=text-to-speech&sort=likes) and the most-liked [**TTS space**](https://huggingface.co/spaces?sort=likes&search=tts) on Hugging Face.
+        This demo only showcases English, but you can directly use the model to access other languages.''', container=True)
+    
     with gr.Row():
         with gr.Column():
             text = gr.Textbox(label='Input Text', info=f"Up to ~500 characters per Generate, or {'∞' if CHAR_LIMIT is None else CHAR_LIMIT} characters per Stream")
@@ -188,11 +198,12 @@ with gr.Blocks() as app:
             speed = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.1, label='Speed')
             random_btn = gr.Button('Random Text', variant='secondary')
         with gr.Column():
-            gr.TabbedInterface([generate_tab], ['Generate'])
-    random_btn.click(fn=get_random_text, inputs=[voice], outputs=[text], api_name=API_NAME)
-    generate_btn.click(fn=generate_first, inputs=[text, voice, speed, use_gpu], outputs=[out_audio, out_ps], api_name=API_NAME)
-    tokenize_btn.click(fn=tokenize_first, inputs=[text, voice], outputs=[out_ps], api_name=API_NAME)
-    predict_btn.click(fn=predict, inputs=[text, voice, speed], outputs=[out_audio], api_name=API_NAME)
+            gr.TabbedInterface([generate_tab, translator_tab], ['Generate', 'Translator'])
+    
+    random_btn.click(fn=get_random_text, inputs=[voice], outputs=[text])
+    generate_btn.click(fn=generate_first, inputs=[text, voice, speed, use_gpu], outputs=[out_audio, out_ps])
+    tokenize_btn.click(fn=tokenize_first, inputs=[text, voice], outputs=[out_ps])
+    predict_btn.click(fn=predict, inputs=[text, voice, speed], outputs=[out_audio])
 
 if __name__ == '__main__':
-    app.queue(api_open=API_OPEN).launch(show_api=API_OPEN, ssr_mode=True)
+    app.queue().launch(show_api=True)