import os #import numpy as np import gradio as gr import whisper import requests import tempfile from neon_tts_plugin_coqui import CoquiTTS # Whisper: Speech-to-text model = whisper.load_model("base") # The LLM : Bloom API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" HF_TOKEN = os.environ["HF_TOKEN"] headers = {"Authorization": f"Bearer {HF_TOKEN}"} # Text-to-Speech LANGUAGES = list(CoquiTTS.langs.keys()) coquiTTS = CoquiTTS() # Processing input Audio def fun(audio) : text1 = model.transcribe(audio)["text"] text2 = lang_model_response(text1) speech = tts(text, language) return text1, text2, speech def lang_model_response(prompt): print(f"*****Inside meme_generate - Prompt is :{prompt}") if len(prompt) == 0: prompt = """Can you help me please?""" json_ = {"inputs": prompt, "parameters": { "top_p": 0.90, #0.90 default "max_new_tokens": 64, "temperature": 1.1, #1.1 default "return_full_text": True, "do_sample": True, }, "options": {"use_cache": True, "wait_for_model": True, },} response = requests.post(API_URL, headers=headers, json=json_) print(f"Response is : {response}") output = response.json() print(f"output is : {output}") output_tmp = output[0]['generated_text'] print(f"output_tmp is: {output_tmp}") solution = output_tmp.split(".")[1] print(f"Final response after splits is: {solution}") return solution #Text-to-Speech def tts(text, language): with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: coquiTTS.get_tts(text, fp, speaker = {"language" : language}) return fp.name #inputs = [gr.Textbox(label="Input", value=CoquiTTS.langs["en"]["sentence"], max_lines=3), # gr.Radio(label="Language", choices=LANGUAGES, value="en")] #outputs = gr.Audio(label="Output") demo = gr.Interface(fn=tts, inputs=inputs, outputs=outputs) demo.launch() gr.Interface( title = 'Testing Whisper', fn=fun, inputs=[ gr.Audio(source="microphone", type="filepath"), #streaming = True, # "state" ], outputs=[ "textbox", "textbox", "audio", ], live=True).launch()