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| import os | |
| os.system("pip install git+https://github.com/openai/whisper.git") | |
| os.system("pip install neon-tts-plugin-coqui==0.6.0") | |
| import gradio as gr | |
| import whisper | |
| import requests | |
| import tempfile | |
| from neon_tts_plugin_coqui import CoquiTTS | |
| from datasets import load_dataset | |
| import random | |
| dataset = load_dataset("ysharma/short_jokes", split="train") | |
| filtered_dataset = dataset.filter( | |
| lambda x: (True not in [nsfw in x["Joke"].lower() for nsfw in ["warning", "fuck", "dead", "nsfw","69", "sex"]]) | |
| ) | |
| # Model 2: Sentence Transformer | |
| API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/msmarco-distilbert-base-tas-b" | |
| HF_TOKEN = os.environ["HF_TOKEN"] | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| def query(payload): | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| return response.json() | |
| # Language common in both the multilingual models - English, Chinese, Spanish, and French etc | |
| # Model 1: Whisper: Speech-to-text | |
| model = whisper.load_model("base") | |
| #Model 2: Text-to-Speech | |
| LANGUAGES = list(CoquiTTS.langs.keys()) | |
| coquiTTS = CoquiTTS() | |
| #Languages for Coqui are: ['en', 'es', 'fr', 'de', 'pl', 'uk', 'ro', 'hu', 'el', 'bg', 'nl', 'fi', 'sl', 'lv', 'ga'] | |
| # Driver function | |
| def driver_fun(audio, text) : | |
| print("*********** Inside Driver ************") | |
| if (text == 'dummy') and (audio is not None) : | |
| print(f"Audio is {audio}") | |
| translation, lang = whisper_stt(audio) | |
| else: | |
| translation = text | |
| random_val = random.randrange(0,231657) | |
| if random_val < 226657: | |
| lower_limit = random_val | |
| upper_limit = random_val + 4000 | |
| else: | |
| lower_limit = random_val - 4000 | |
| upper_limit = random_val | |
| print(f"lower_limit : upper_limit = {lower_limit} : {upper_limit}") | |
| dataset_subset = filtered_dataset['Joke'][lower_limit : upper_limit] | |
| data = query({"inputs": {"source_sentence": translation ,"sentences": dataset_subset} } ) #"That is a happy person" | |
| if 'error' in data: | |
| print(f"Error is : {data}") | |
| return 'Error in model inference - Run Again Please', 'Error in model inference - Run Again Please', None | |
| print(f"type(data) : {type(data)}") | |
| #print(f"data : {data} ") | |
| max_match_score = max(data) | |
| indx_score = data.index(max_match_score) | |
| joke = dataset_subset[indx_score] | |
| print(f"Joke is : {joke}") | |
| speech = tts(joke, 'en') | |
| return translation, joke, speech | |
| # Whisper - speech-to-text | |
| def whisper_stt(audio): | |
| print("Inside Whisper TTS") | |
| # load audio and pad/trim it to fit 30 seconds | |
| audio = whisper.load_audio(audio) | |
| audio = whisper.pad_or_trim(audio) | |
| # make log-Mel spectrogram and move to the same device as the model | |
| mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
| # detect the spoken language | |
| _, probs = model.detect_language(mel) | |
| lang = max(probs, key=probs.get) | |
| print(f"Detected language: {max(probs, key=probs.get)}") | |
| # decode the audio | |
| options_transl = whisper.DecodingOptions(fp16 = False, language='en', task='translate') #lang | |
| result_transl = whisper.decode(model, mel, options_transl) #model_med | |
| # print the transcribed text | |
| print(f"translation is : {result_transl.text}") | |
| return result_transl.text, lang | |
| # Coqui - Text-to-Speech | |
| def tts(text, language): | |
| print(f"Inside tts - language is : {language}") | |
| print(f"Text is : {text}") | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: | |
| coquiTTS.get_tts(text, fp, speaker = {"language" : language}) | |
| return fp.name | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("<h1><center>AI Assistant - Voice to Joke</center></h1>") | |
| gr.Markdown( | |
| """<center>Just record <i><b>"Hey Whisper can you tell me a joke on X please?"</i></b>, X = anything you would wish.</center><br><center>Or, press record and just utter a theme. If you see the message 'Error in model inference - Run Again Please', just press the button again every time!</center> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| in_audio = gr.Audio(source="microphone", type="filepath", label='Record your voice command here in English -') #type='filepath' | |
| b1 = gr.Button("AI Response") | |
| out_transcript = gr.Textbox(label= 'Transcript of your Audio using OpenAI Whisper') | |
| with gr.Column(): | |
| in_text = gr.Textbox(label='Or enter any text here..', value='dummy') | |
| out_audio = gr.Audio(label='Audio response form CoquiTTS') | |
| out_generated_joke = gr.Textbox(label= 'Joke returned! ') | |
| b1.click(driver_fun,inputs=[in_audio, in_text], outputs=[out_transcript, out_generated_joke, out_audio]) #out_translation_en, out_generated_text,out_generated_text_en, | |
| with gr.Row(): | |
| gr.Markdown( | |
| """Model pipeline consisting of - <br>- [**Whisper**](https://github.com/openai/whisper) for Speech-to-text, <br>- [**CoquiTTS**](https://huggingface.co/coqui) for Text-To-Speech.<br>- [Sentence Transformers](https://huggingface.co/models?library=sentence-transformers&sort=downloads)<br>- Front end is built using [**Gradio Block API**](https://gradio.app/docs/#blocks).<br><be>If you want to reuse the App, simply click on the small cross button in the top right corner of your voice record panel, and then press record again! <br><br> Few Caveats:<br>1. Please note that sometimes the joke might be NSFW. Although, I have tried putting in filters to not have that experience, but they seem non-exhaustive.<br>2. Sometimes the joke might not match your theme, please bear with the limited capabilities of free open-source ML prototypes.<br>3. Much like real life, sometimes the joke might just not land, haha!<br>4. Repeating this: If you see the message 'Error in model inference - Run Again Please', just press the button again every time! | |
| """) | |
| demo.launch(enable_queue=True, debug=True) |