Audio Output Streaming

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import requests
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DEEPGRAM_URL = "https://api.deepgram.com/v1/speak?model=aura-asteria-en"
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DEEPGRAM_API_KEY = "DEEPGRAM_API_KEY"
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payload = {
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    "text": "Hello, how can I help you today? My name is Emily and I'm very glad to meet you. What do you think of this new text-to-speech API?"
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}
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headers = {
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    "Authorization": f"Token {DEEPGRAM_API_KEY}",
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    "Content-Type": "application/json"
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}
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audio_file_path = "output.mp3"  # Path to save the audio file
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with open(audio_file_path, 'wb') as file_stream:
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    response = requests.post(DEEPGRAM_URL, headers=headers, json=payload, stream=True)
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    for chunk in response.iter_content(chunk_size=1024):
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        if chunk:
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            file_stream.write(chunk) # Write each chunk of audio data to the file
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print("Audio download complete")

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import re
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import requests
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DEEPGRAM_URL = 'https://api.deepgram.com/v1/speak?model=aura-helios-en'
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headers = {
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    "Authorization": "Token DEEPGRAM_API_KEY",
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    "Content-Type": "application/json"
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}
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input_text = "Our story begins in a peaceful woodland kingdom where a lively squirrel named Frolic made his abode high up within a cedar tree's embrace. He was not a usual woodland creature, for he was blessed with an insatiable curiosity and a heart for adventure. Nearby, a glistening river snaked through the landscape, home to a wonder named Splash - a silver-scaled flying fish whose ability to break free from his water-haven intrigued the woodland onlookers. This magical world moved on a rhythm of its own until an unforeseen circumstance brought Frolic and Splash together. One radiant morning, while Frolic was on his regular excursion, and Splash was making his aerial tours, an unpredictable wave playfully tossed and misplaced Splash onto the riverbank. Despite his initial astonishment, Frolic hurriedly and kindly assisted his new friend back to his watery abode. Touched by Frolic's compassion, Splash expressed his gratitude by inviting his friend to share his world. As Splash perched on Frolic's back, he tasted of the forest's bounty, felt the sun’s rays filter through the colors of the trees, experienced the conversations amidst the woods, and while at it, taught the woodland how to blur the lines between earth and water."
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def segment_text_by_sentence(text):
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    sentence_boundaries = re.finditer(r'(?<=[.!?])\s+', text)
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    boundaries_indices = [boundary.start() for boundary in sentence_boundaries]
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    segments = []
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    start = 0
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    for boundary_index in boundaries_indices:
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        segments.append(text[start:boundary_index + 1].strip())
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        start = boundary_index + 1
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    segments.append(text[start:].strip())
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    return segments
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def synthesize_audio(text, output_file):
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    payload = {"text": text}
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    with requests.post(DEEPGRAM_URL, stream=True, headers=headers, json=payload) as r:
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        for chunk in r.iter_content(chunk_size=1024):
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            if chunk:
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                output_file.write(chunk)
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def main():
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    segments = segment_text_by_sentence(input_text)
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    # Create or truncate the output file
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    with open("output.mp3", "wb") as output_file:
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        for segment_text in segments:
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            synthesize_audio(segment_text, output_file)
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    print("Audio file creation completed.")
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if __name__ == "__main__":
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    main()

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class BufferPredictor:
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    def __init__(self):
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        self.buffer_size = DEFAULT_BUFFER_SIZE
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    def adjust_buffer_size(self, network_latency, bandwidth):
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        # Example buffer prediction algorithm based on network latency and bandwidth
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        predicted_buffer_size = calculate_buffer_size(network_latency, bandwidth)
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        self.buffer_size = predicted_buffer_size
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# Usage
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buffer_predictor = BufferPredictor()
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network_latency = get_network_latency()
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bandwidth = get_available_bandwidth()
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buffer_predictor.adjust_buffer_size(network_latency, bandwidth)
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print("Adjusted buffer size:", buffer_predictor.buffer_size)