Update app.py

app.py

@@ -1,82 +1,31 @@
 import gradio as gr
-from TTS.api import TTS
+# from TTS.api import TTS
 
-tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
+# tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
 
-def predict(text):
-    file_path = "output.wav"
-    tts.tts_to_file(text, speaker=tts.speakers[0], language="en", file_path=file_path)
-    return file_path
-
-demo = gr.Interface(
-    fn=predict,
-    inputs='text',
-    outputs='audio'
-)
-
-
-demo.launch()
-
-
-# import requests
-# import time
-# import tempfile
-# import os
-
-# token = os.environ['apikey']
-# #discord_id = os.environ['discord-id']
-# API_HOST = "https://labs-proxy.voicemod.net/"
-
-# def download_file(url):
-#     response = requests.get(url)
-#     if response.status_code == 200:
-#         with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-#             tmp_file.write(response.content)
-#             tmp_file.flush()
-#             return tmp_file.name
-#     else:
-#         print("Error: Unable to download file")
-
-# def tts(text):
-#     url = API_HOST + "api/v1/tts/create"
-#     payload = {
-#         "text": text[:200] if len(text) > 200 else text,
-#         "voiceId": "6926ecc5-ff5e-47c6-912b-3ffdb880bf56" # Narrator
-#     }
-#     headers = {
-#         'x-api-key': token,
-#     }
-#     response = requests.request("POST", url, headers=headers, json=payload)
-#     jsonResp = response.json()
-
-#     return gr.make_waveform(download_file(jsonResp['audioUrl']))
+# def predict(text):
+#     file_path = "output.wav"
+#     tts.tts_to_file(text, speaker=tts.speakers[0], language="en", file_path=file_path)
+#     return file_path
 
 # demo = gr.Interface(
-#     fn=tts,
-#     inputs="text",
-#     outputs="audio"
+#     fn=predict,
+#     inputs='text',
+#     outputs='audio'
 # )
 
-# demo.launch()
 
-# import openai
-# import os
-# #from pymongo import MongoClient
+# demo.launch()
 
-# api_key = os.environ.get("OPENAI_API_KEY")
 
-# def transcribe_audio(filepath):
-#     audio = open(filepath, "rb")
-#     transcript = openai.Audio.transcribe("whisper-1", audio)
-#     return transcript['text']
-    
+import librosa
+import numpy as np
+import torch
 
-# # Create a Gradio Tabbed Interface
-# iface = gr.Interface(
-#     fn=transcribe_audio,
-#     inputs= gr.Audio(source="upload", type="filepath"),
-#     outputs="text",
-# )
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
 
 
-# iface.launch()
+checkpoint = "microsoft/speecht5_tts"
+processor = SpeechT5Processor.from_pretrained(checkpoint)
+model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")