| # import gradio as gr | |
| # import requests | |
| # import os | |
| # def function1(prompt): | |
| # response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={ | |
| # "data": [ | |
| # prompt, | |
| # ]}).json() | |
| # message = response["data"][0] | |
| # url = 'https://api.elevenlabs.io/v1/text-to-speech/pNInz6obpgDQGcFmaJgB' | |
| # headers = { | |
| # 'accept': 'audio/mpeg', | |
| # 'xi-api-key': os.environ.get("test2"), | |
| # 'Content-Type': 'application/json' | |
| # } | |
| # data = { | |
| # "text": message, | |
| # "voice_settings": { | |
| # "stability": 0, | |
| # "similarity_boost": 0 | |
| # } | |
| # } | |
| # response = requests.post(url, headers=headers, json=data) | |
| # if response.status_code == 200: | |
| # file_path = 'test.mp3' | |
| # if os.path.isfile(file_path): | |
| # os.remove(file_path) | |
| # with open(file_path, 'wb') as f: | |
| # f.write(response.content) | |
| # return "test.mp3" | |
| # iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")]) | |
| # iface.launch() | |
| # import gradio as gr | |
| # import requests | |
| # import urllib.request | |
| # from pydub import AudioSegment | |
| # import numpy as np | |
| # import os | |
| # def function1(prompt): | |
| # response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={ | |
| # "data": [ | |
| # prompt, | |
| # ]}).json() | |
| # data = response["data"][0] | |
| # response = requests.post("https://matthijs-speecht5-tts-demo.hf.space/run/predict", json={ | |
| # "data": [ | |
| # data, | |
| # "KSP (male)", | |
| # ] | |
| # }).json() | |
| # data = response["data"][0]["name"] | |
| # data = "https://matthijs-speecht5-tts-demo.hf.space/file="+data | |
| # file_name, headers = urllib.request.urlretrieve(data, "speech.mp3") | |
| # # code = random.randint(1,1000) | |
| # # generated_file = f"output{code}" | |
| # filename = "output.mp3" | |
| # if os.path.exists(filename): | |
| # os.remove(filename) | |
| # else: | |
| # pass | |
| # command = f"ffmpeg -i {file_name} -vn -ar 44100 -ac 2 -b:a 192k output.mp3" | |
| # os.system(command) | |
| # return "output.mp3" | |
| # iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")]) | |
| # iface.launch() | |
| import gradio as gr | |
| import requests | |
| import urllib.request | |
| from pydub import AudioSegment | |
| import numpy as np | |
| import os | |
| import sys | |
| import wave | |
| import io | |
| import base64 | |
| import azure.cognitiveservices.speech as speechsdk | |
| speech_key = os.environ.get("test3") | |
| service_region = os.environ.get("test4") | |
| speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region) | |
| # Note: the voice setting will not overwrite the voice element in input SSML. | |
| speech_config.speech_synthesis_voice_name = os.environ.get("test5") | |
| def function1(prompt): | |
| response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={ | |
| "data": [ | |
| prompt, | |
| ]}).json() | |
| message = response["data"][0] | |
| speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config) | |
| result = speech_synthesizer.speak_text_async(message).get() | |
| if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted: | |
| audio_stream = io.BytesIO(result.audio_data) | |
| # Create a wave file object and write the audio data to it | |
| with wave.open("audio.wav", 'wb') as wave_file: | |
| wave_file.setnchannels(1) | |
| wave_file.setsampwidth(2) | |
| wave_file.setframerate(16000) | |
| wave_file.writeframesraw(audio_stream.getvalue()) | |
| # Use ffmpeg to convert the wave file to an mp3 file | |
| filename = "output.mp3" | |
| if os.path.exists(filename): | |
| os.remove(filename) | |
| else: | |
| pass | |
| command = f"ffmpeg -i audio.wav -y -codec:a libmp3lame -qscale:a 2 {filename}" | |
| os.system(command) | |
| return "output.mp3" | |
| iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")]) | |
| iface.launch() |