| import gradio as gr | |
| import edge_tts | |
| import tempfile | |
| import numpy as np | |
| import soxr | |
| from pydub import AudioSegment | |
| import torch | |
| import sentencepiece as spm | |
| import onnxruntime as ort | |
| from huggingface_hub import hf_hub_download, InferenceClient | |
| theme = gr.themes.Soft( | |
| primary_hue="blue", | |
| secondary_hue="orange") | |
| # Speech Recognition Model Configuration | |
| model_name = "neongeckocom/stt_en_citrinet_512_gamma_0_25" | |
| sample_rate = 16000 | |
| # Download preprocessor, encoder and tokenizer | |
| preprocessor = torch.jit.load(hf_hub_download(model_name, "preprocessor.ts", subfolder="onnx")) | |
| encoder = ort.InferenceSession(hf_hub_download(model_name, "model.onnx", subfolder="onnx")) | |
| tokenizer = spm.SentencePieceProcessor(hf_hub_download(model_name, "tokenizer.spm", subfolder="onnx")) | |
| # Mistral Model Configuration | |
| client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
| system_instructions1 = "<s>[SYSTEM] Answer as OpenGPT 4o, Made by 'Mazen', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide Funny responses. The expectation is that I will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]" | |
| def resample(audio_fp32, sr): | |
| return soxr.resample(audio_fp32, sr, sample_rate) | |
| def to_float32(audio_buffer): | |
| return np.divide(audio_buffer, np.iinfo(audio_buffer.dtype).max, dtype=np.float32) | |
| async def transcribe(audio_path): | |
| audio_file = AudioSegment.from_file(audio_path) | |
| sr = audio_file.frame_rate | |
| audio_buffer = np.array(audio_file.get_array_of_samples()) | |
| audio_fp32 = to_float32(audio_buffer) | |
| audio_16k = resample(audio_fp32, sr) | |
| input_signal = torch.tensor(audio_16k).unsqueeze(0) | |
| length = torch.tensor(len(audio_16k)).unsqueeze(0) | |
| processed_signal, _ = preprocessor.forward(input_signal=input_signal, length=length) | |
| logits = encoder.run(None, {'audio_signal': processed_signal.numpy(), 'length': length.numpy()})[0][0] | |
| blank_id = tokenizer.vocab_size() | |
| decoded_prediction = [p for p in logits.argmax(axis=1).tolist() if p != blank_id] | |
| text = tokenizer.decode_ids(decoded_prediction) | |
| return text | |
| async def model(text): | |
| formatted_prompt = system_instructions1 + text + "[OpenGPT 4o]" | |
| stream = client1.text_generation(formatted_prompt, max_new_tokens=512, stream=True, details=True, return_full_text=False) | |
| return "".join([response.token.text for response in stream if response.token.text != "</s>"]) | |
| async def respond(audio): | |
| user = await transcribe(audio) | |
| reply = await model(user) | |
| communicate = edge_tts.Communicate(reply, voice="en-US-JennyNeural") # Example voice | |
| ##communicate = edge_tts.Communicate(reply) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: | |
| tmp_path = tmp_file.name | |
| await communicate.save(tmp_path) | |
| return tmp_path | |
| with gr.Blocks(theme=theme) as demo: | |
| input = gr.Audio(label="User Input", sources="microphone", type="filepath") | |
| output = gr.Audio(label="AI", autoplay=True) | |
| gr.Interface(fn=respond, inputs=[input], outputs=[output], live=True) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=200).launch() | |
| # import gradio as gr | |
| # import edge_tts | |
| # import asyncio | |
| # import tempfile | |
| # import numpy as np | |
| # import soxr | |
| # from pydub import AudioSegment | |
| # import torch | |
| # import sentencepiece as spm | |
| # import onnxruntime as ort | |
| # from huggingface_hub import hf_hub_download, InferenceClient | |
| # import requests | |
| # from bs4 import BeautifulSoup | |
| # import urllib | |
| # import random | |
| # theme = gr.themes.Soft( | |
| # primary_hue="blue", | |
| # secondary_hue="orange") | |
| # # List of user agents to choose from for requests | |
| # _useragent_list = [ | |
| # 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0', | |
| # 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36', | |
| # 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36', | |
| # 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36', | |
| # 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36', | |
| # 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.62', | |
| # 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0' | |
| # ] | |
| # def get_useragent(): | |
| # """Returns a random user agent from the list.""" | |
| # return random.choice(_useragent_list) | |
| # def extract_text_from_webpage(html_content): | |
| # """Extracts visible text from HTML content using BeautifulSoup.""" | |
| # soup = BeautifulSoup(html_content, "html.parser") | |
| # # Remove unwanted tags | |
| # for tag in soup(["script", "style", "header", "footer", "nav"]): | |
| # tag.extract() | |
| # # Get the remaining visible text | |
| # visible_text = soup.get_text(strip=True) | |
| # return visible_text | |
| # def search(term, num_results=1, lang="en", advanced=True, sleep_interval=0, timeout=5, safe="active", ssl_verify=None): | |
| # """Performs a Google search and returns the results.""" | |
| # escaped_term = urllib.parse.quote_plus(term) | |
| # start = 0 | |
| # all_results = [] | |
| # # Fetch results in batches | |
| # while start < num_results: | |
| # resp = requests.get( | |
| # url="https://www.google.com/search", | |
| # headers={"User-Agent": get_useragent()}, # Set random user agent | |
| # params={ | |
| # "q": term, | |
| # "num": num_results - start, # Number of results to fetch in this batch | |
| # "hl": lang, | |
| # "start": start, | |
| # "safe": safe, | |
| # }, | |
| # timeout=timeout, | |
| # verify=ssl_verify, | |
| # ) | |
| # resp.raise_for_status() # Raise an exception if request fails | |
| # soup = BeautifulSoup(resp.text, "html.parser") | |
| # result_block = soup.find_all("div", attrs={"class": "g"}) | |
| # # If no results, continue to the next batch | |
| # if not result_block: | |
| # start += 1 | |
| # continue | |
| # # Extract link and text from each result | |
| # for result in result_block: | |
| # link = result.find("a", href=True) | |
| # if link: | |
| # link = link["href"] | |
| # try: | |
| # # Fetch webpage content | |
| # webpage = requests.get(link, headers={"User-Agent": get_useragent()}) | |
| # webpage.raise_for_status() | |
| # # Extract visible text from webpage | |
| # visible_text = extract_text_from_webpage(webpage.text) | |
| # all_results.append({"link": link, "text": visible_text}) | |
| # except requests.exceptions.RequestException as e: | |
| # # Handle errors fetching or processing webpage | |
| # print(f"Error fetching or processing {link}: {e}") | |
| # all_results.append({"link": link, "text": None}) | |
| # else: | |
| # all_results.append({"link": None, "text": None}) | |
| # start += len(result_block) # Update starting index for next batch | |
| # return all_results | |
| # # Speech Recognition Model Configuration | |
| # model_name = "neongeckocom/stt_en_citrinet_512_gamma_0_25" | |
| # sample_rate = 16000 | |
| # # Download preprocessor, encoder and tokenizer | |
| # preprocessor = torch.jit.load(hf_hub_download(model_name, "preprocessor.ts", subfolder="onnx")) | |
| # encoder = ort.InferenceSession(hf_hub_download(model_name, "model.onnx", subfolder="onnx")) | |
| # tokenizer = spm.SentencePieceProcessor(hf_hub_download(model_name, "tokenizer.spm", subfolder="onnx")) | |
| # # Mistral Model Configuration | |
| # client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
| # system_instructions1 = "<s>[SYSTEM] Answer as OpenGPT 4o, Made by 'KingNish', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses. The expectation is that I will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]" | |
| # def resample(audio_fp32, sr): | |
| # return soxr.resample(audio_fp32, sr, sample_rate) | |
| # def to_float32(audio_buffer): | |
| # return np.divide(audio_buffer, np.iinfo(audio_buffer.dtype).max, dtype=np.float32) | |
| # def transcribe(audio_path): | |
| # audio_file = AudioSegment.from_file(audio_path) | |
| # sr = audio_file.frame_rate | |
| # audio_buffer = np.array(audio_file.get_array_of_samples()) | |
| # audio_fp32 = to_float32(audio_buffer) | |
| # audio_16k = resample(audio_fp32, sr) | |
| # input_signal = torch.tensor(audio_16k).unsqueeze(0) | |
| # length = torch.tensor(len(audio_16k)).unsqueeze(0) | |
| # processed_signal, _ = preprocessor.forward(input_signal=input_signal, length=length) | |
| # logits = encoder.run(None, {'audio_signal': processed_signal.numpy(), 'length': length.numpy()})[0][0] | |
| # blank_id = tokenizer.vocab_size() | |
| # decoded_prediction = [p for p in logits.argmax(axis=1).tolist() if p != blank_id] | |
| # text = tokenizer.decode_ids(decoded_prediction) | |
| # return text | |
| # def model(text, web_search): | |
| # if web_search is True: | |
| # """Performs a web search, feeds the results to a language model, and returns the answer.""" | |
| # web_results = search(text) | |
| # web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results]) | |
| # formatted_prompt = system_instructions1 + text + "[WEB]" + str(web2) + "[OpenGPT 4o]" | |
| # stream = client1.text_generation(formatted_prompt, max_new_tokens=512, stream=True, details=True, return_full_text=False) | |
| # return "".join([response.token.text for response in stream if response.token.text != "</s>"]) | |
| # else: | |
| # formatted_prompt = system_instructions1 + text + "[OpenGPT 4o]" | |
| # stream = client1.text_generation(formatted_prompt, max_new_tokens=512, stream=True, details=True, return_full_text=False) | |
| # return "".join([response.token.text for response in stream if response.token.text != "</s>"]) | |
| # async def respond(audio, web_search): | |
| # user = transcribe(audio) | |
| # reply = model(user, web_search) | |
| # communicate = edge_tts.Communicate(reply) | |
| # with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: | |
| # tmp_path = tmp_file.name | |
| # await communicate.save(tmp_path) | |
| # return tmp_path | |
| # with gr.Blocks(theme=theme) as demo: | |
| # with gr.Row(): | |
| # web_search = gr.Checkbox(label="Web Search", value=False) | |
| # input = gr.Audio(label="User Input", sources="microphone", type="filepath") | |
| # output = gr.Audio(label="AI", autoplay=True) | |
| # gr.Interface(fn=respond, inputs=[input, web_search], outputs=[output], live=True) | |
| # if __name__ == "__main__": | |
| # demo.queue(max_size=200).launch() | |