Update handler.py
Browse files- handler.py +3 -29
handler.py
CHANGED
@@ -4,9 +4,7 @@ import torch
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import soundfile as sf
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from transformers import AutoTokenizer, AutoModelForTextToWaveform
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import cloudinary.uploader
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import pygame
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import tkinter as tk
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# Configure logging
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logging.basicConfig(level=logging.DEBUG)
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@@ -21,7 +19,7 @@ class EndpointHandler():
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self.tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
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self.model= AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-eng")
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def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
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# Prepare the payload with input data
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logging.warning(f"------input_data-- {str(data)}")
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@@ -37,7 +35,7 @@ class EndpointHandler():
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# Save the audio to a file
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sf.write("StoryAudio.wav", outputs["waveform"][0].numpy(), self.model.config.sampling_rate)
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uploadGraphFile("StoryAudio.wav")
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#return 'StoryAudio.wav'
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# Check if the request was successful
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@@ -51,27 +49,3 @@ def uploadGraphFile(fileName):
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# Upload a file to Cloudinary
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result = cloudinary.uploader.upload(fileName, folder="poc-graph", resource_type="raw")
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return result
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def play_audio():
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pygame.mixer.music.load("StoryAudio.wav")
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pygame.mixer.music.play()
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def stop_audio():
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pygame.mixer.music.stop()
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def playAudio():
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root = tk.Tk()
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root.title("Audio Player")
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# Create a play button
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play_button = tk.Button(root, text="Play", command=play_audio)
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play_button.pack()
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# Create a stop button
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stop_button = tk.Button(root, text="Stop", command=stop_audio)
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stop_button.pack()
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# Create a progress bar
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progress_bar = ttk.Progressbar(root, orient="horizontal", length=200, mode="determinate")
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progress_bar.pack()
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root.mainloop()
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import soundfile as sf
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from transformers import AutoTokenizer, AutoModelForTextToWaveform
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import cloudinary.uploader
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# Configure logging
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logging.basicConfig(level=logging.DEBUG)
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self.tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
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self.model= AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-eng")
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def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
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# Prepare the payload with input data
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logging.warning(f"------input_data-- {str(data)}")
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# Save the audio to a file
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sf.write("StoryAudio.wav", outputs["waveform"][0].numpy(), self.model.config.sampling_rate)
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uploadGraphFile("StoryAudio.wav")
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#return 'StoryAudio.wav'
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# Check if the request was successful
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# Upload a file to Cloudinary
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result = cloudinary.uploader.upload(fileName, folder="poc-graph", resource_type="raw")
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return result
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