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import tempfile
import gradio as gr
from neon_tts_plugin_coqui import CoquiTTS
LANGUAGES = list(CoquiTTS.langs.keys())
LANGUAGES = LANGUAGES + ['cn', 'jp']
default_lang = "en"
#import whisper
#whisper_model = whisper.load_model("small")
whisper = gr.Interface.load(name="spaces/abidlabs/whisper-large-v2")
#whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2")
#cn_a_jp = gr.Blocks.load(name="spaces/Yusin/anime-tts_yusin")
#chatgpt = gr.Blocks.load(name="spaces/fffiloni/whisper-to-chatGPT")
#chatgpt = gr.Blocks.load(name="spaces/seawolf2357/chatgptclone")
import os
import json
import openai
#session_token = os.environ.get('SessionToken')
api_key = os.environ.get('api_key')
#if you have OpenAI API key as a string, enable the below
openai.api_key = api_key
title = "Speech to ChatGPT to Speech"
#info = "more info at [Neon Coqui TTS Plugin](https://github.com/NeonGeckoCom/neon-tts-plugin-coqui), [Coqui TTS](https://github.com/coqui-ai/TTS)"
#badge = "https://visitor-badge-reloaded.herokuapp.com/badge?page_id=neongeckocom.neon-tts-plugin-coqui"
coquiTTS = CoquiTTS()
# ChatGPT
def chat_hf(audio, custom_token, language):
try:
whisper_text = translate(audio)
if whisper_text == "ERROR: You have to either use the microphone or upload an audio file":
gpt_response = "MISSING AUDIO: Record your voice by clicking the microphone button, do not forget to stop recording before sending your message ;)"
else:
#gpt_response = chatgpt(whisper_text, [], fn_index=0)
#print(gpt_response)
#gpt_response = gpt_response[0]
gpt_response = openai_create(whisper_text)
except:
whisper_text = translate(audio)
gpt_response = """Sorry, I'm quite busy right now, but please try again later :)"""
# to voice
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
coquiTTS.get_tts(gpt_response, fp, speaker = {"language" : language})
return whisper_text, gpt_response, fp.name
# whisper
#def translate(audio):
# print("""
# β
# Sending audio to Whisper ...
# β
# """)
#
# audio = whisper.load_audio(audio)
# audio = whisper.pad_or_trim(audio)
#
# mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
#
# _, probs = whisper_model.detect_language(mel)
#
# transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False)
#
# transcription = whisper.decode(whisper_model, mel, transcript_options)
#
# print("language spoken: " + transcription.language)
# print("transcript: " + transcription.text)
# print("βββββββββββββββββββββββββββββββββββββββββββ")
#
# return transcription.text
def translate(audio):
print("""
β
Sending audio to Whisper ...
β
""")
text_result = whisper(audio, None, "transcribe", fn_index=0)
#_, text_result = whisper(audio, "", fn_index=0)
print(text_result)
return text_result
def openai_create(prompt):
response = openai.Completion.create(
model="text-davinci-003",
prompt=prompt,
temperature=0.9,
max_tokens=150,
top_p=1,
frequency_penalty=0,
presence_penalty=0.6,
stop=[" Human:", " AI:"]
)
print(response.choices[0].text)
return response.choices[0].text
with gr.Blocks() as blocks:
gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>"
+ title
+ "</h1>")
#gr.Markdown(description)
radio = gr.Radio(label="Language", choices=LANGUAGES, value=default_lang)
with gr.Row(equal_height=True):# equal_height=False
with gr.Column():# variant="panel"
audio_file = gr.Audio(source="microphone", type="filepath")
custom_token = gr.Textbox(label='If it fails, use your own session token', placeholder="your own session token")
with gr.Row():# mobile_collapse=False
submit = gr.Button("Submit", variant="primary")
with gr.Column():
text1 = gr.Textbox(label="Speech to Text")
text2 = gr.Textbox(label="ChatGPT Response")
audio = gr.Audio(label="Output", interactive=False)
#gr.Markdown(info)
#gr.Markdown("<center>"
# +f'<img src={badge} alt="visitors badge"/>'
# +"</center>")
# actions
submit.click(
chat_hf,
[audio_file, custom_token, radio],
[text1, text2, audio],
)
#radio.change(lambda lang: CoquiTTS.langs[lang]["sentence"], radio, text2)
blocks.launch(debug=True)
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