import os import gradio as gr import whisper import requests import tempfile from neon_tts_plugin_coqui import CoquiTTS # Language common in all three multilingual models - English, Chinese, Spanish, and French # So it would make sense to test the App on these four prominently # Whisper: Speech-to-text model = whisper.load_model("base") model_med = whisper.load_model("medium") # Languages covered in Whisper - (exhaustive list) : #"en": "english", "zh": "chinese", "de": "german", "es": "spanish", "ru": "russian", #"ko": "korean", "fr": "french", "ja": "japanese", "pt": "portuguese", "tr": "turkish", #"pl": "polish", "ca": "catalan", "nl": "dutch", "ar": "arabic", "sv": "swedish", #"it": "italian", "id": "indonesian", "hi": "hindi", "fi": "finnish", "vi": "vietnamese", #"iw": "hebrew", "uk": "ukrainian", "el": "greek", "ms": "malay", "cs": "czech", #"ro": "romanian", "da": "danish", "hu": "hungarian", "ta": "tamil", "no": "norwegian", #"th": "thai", "ur": "urdu", "hr": "croatian", "bg": "bulgarian", "lt": "lithuanian", #"la": "latin", "mi": "maori", "ml": "malayalam", "cy": "welsh", "sk": "slovak", #"te": "telugu", "fa": "persian", "lv": "latvian", "bn": "bengali", "sr": "serbian", #"az": "azerbaijani", "sl": "slovenian", "kn": "kannada", "et": "estonian", #"mk": "macedonian", "br": "breton", "eu": "basque", "is": "icelandic", "hy": "armenian", #"ne": "nepali", "mn": "mongolian", "bs": "bosnian", "kk": "kazakh", "sq": "albanian", #"sw": "swahili", "gl": "galician", "mr": "marathi", "pa": "punjabi", "si": "sinhala", #"km": "khmer", "sn": "shona", "yo": "yoruba", "so": "somali", "af": "afrikaans", #"oc": "occitan", "ka": "georgian", "be": "belarusian", "tg": "tajik", "sd": "sindhi", #"gu": "gujarati", "am": "amharic", "yi": "yiddish", "lo": "lao", "uz": "uzbek", #"fo": "faroese", "ht": "haitian creole", "ps": "pashto", "tk": "turkmen", "nn": "nynorsk", #"mt": "maltese", "sa": "sanskrit", "lb": "luxembourgish", "my": "myanmar", "bo": "tibetan", #"tl": "tagalog", "mg": "malagasy", "as": "assamese", "tt": "tatar", "haw": "hawaiian", #"ln": "lingala", "ha": "hausa", "ba": "bashkir", "jw": "javanese", "su": "sundanese", # LLM : Bloom as inference API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" HF_TOKEN = os.environ["HF_TOKEN"] headers = {"Authorization": f"Bearer {HF_TOKEN}"} # Main Languages covered in Bloom are (not exhaustive list): # English, Chinese, French, Spanish, Portuguese, Arabic, Hindi, Vietnamese, Indonesian, Bengali, Tamil, Telugu # Text-to-Speech LANGUAGES = list(CoquiTTS.langs.keys()) coquiTTS = CoquiTTS() print(f"Languages for Coqui are: {LANGUAGES}") #Languages for Coqui are: ['en', 'es', 'fr', 'de', 'pl', 'uk', 'ro', 'hu', 'el', 'bg', 'nl', 'fi', 'sl', 'lv', 'ga'] # en - Engish, es - Spanish, fr - French, de - German, pl - Polish # uk - Ukrainian, ro - Romanian, hu - Hungarian, el - Greek, bg - Bulgarian, # nl - dutch, fi - finnish, sl - slovenian, lv - latvian, ga - ?? # Driver function def driver_fun(audio) : transcribe, translation, lang = whisper_stt(audio) #text1 = model.transcribe(audio)["text"] #For now only taking in English text for Bloom prompting as inference model is not high spec text_generated = lang_model_response(transcribe, lang) text_generated_en = lang_model_response(translation, 'en') if lang in ['es', 'fr']: speech = tts(text_generated, lang) else: speech = tts(text_generated_en, 'en') #'en') return transcribe, translation, text_generated, text_generated_en, 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_transc = whisper.DecodingOptions(fp16 = False, language=lang, task='transcribe') #lang options_transl = whisper.DecodingOptions(fp16 = False, language='en', task='translate') #lang result_transc = whisper.decode(model_med, mel, options_transc) result_transl = whisper.decode(model_med, mel, options_transl) # print the recognized text print(f"transcript is : {result_transc.text}") print(f"translation is : {result_transl.text}") return result_transc.text, result_transl.text, lang # LLM - Bloom Response def lang_model_response(prompt, language): print(f"Inside lang_model_response - Prompt is :{prompt}") p_en = """Question: How are you doing today? Answer: I am doing good, thanks. Question: """ p_es = """Pregunta: Cómo estás hoy? Responder: Estoy bien, gracias. Pregunta: """ p_fr = """Question: Comment vas-tu aujourd'hui? Réponse: Je vais bien, merci. Question: """ if len(prompt) == 0: prompt = """Question: Can you help me please? Answer: Sure, I am here for you. Question: """ if language == 'en': prompt = p_en + prompt + "\n" + "Answer: " elif language == 'es': prompt = p_es + prompt + "\n" + "Responder: " elif language == 'fr': prompt = p_fr + prompt + "\n" + "Réponse: " json_ = {"inputs": prompt, "parameters": { "top_p": 0.90, #0.90 default "max_new_tokens": 64, "temperature": 1.1, #1.1 default "return_full_text": False, "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() output_tmp = output[0]['generated_text'] print(f"Bloom API Response is : {output_tmp}") #if language == 'en': solution = output_tmp.split("Answer: ")[2].split("\n")[0] #else: # solution = output_tmp.split(".")[1] print(f"Final Bloom Response after splits is: {solution}") return solution # Coqui - Text-to-Speech def tts(text, language): print(f"Inside tts - language is : {language}") coqui_langs = ['en' ,'es' ,'fr' ,'de' ,'pl' ,'uk' ,'ro' ,'hu' ,'bg' ,'nl' ,'fi' ,'sl' ,'lv' ,'ga'] if language not in coqui_langs: language = 'en' 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("

Talk to Your Multilingual AI Assistant

") gr.Markdown( """Model pipeline consisting of -
- **Whisper** for Speech-to-text,
- **Bloom** for Text-generation, and
- **CoquiTTS** for Text-To-Speech.

Front end is built using **Gradio Block API**. """) with gr.Row(): with gr.Column(): in_audio = gr.Audio(source="microphone", type="filepath", label='Record your voice here') #type='filepath' b1 = gr.Button("AI response (Whisper - Bloom - Coqui pipeline)") out_transcript = gr.Textbox(label= 'As is Transcript using OpenAI Whisper') out_translation_en = gr.Textbox(label= 'English Translation of audio using OpenAI Whisper') with gr.Column(): out_audio = gr.Audio(label='AI response in Audio form in your preferred language') out_generated_text = gr.Textbox(label= 'AI response to your query in your preferred language using Bloom! ') out_generated_text_en = gr.Textbox(label= 'AI response to your query in English using Bloom! ') b1.click(driver_fun,inputs=[in_audio], outputs=[out_transcript, out_translation_en, out_generated_text,out_generated_text_en, out_audio]) demo.launch(enable_queue=True, debug=True) #gr.Interface( # title = 'Testing Whisper', # fn=driver_fun, # inputs=[ # gr.Audio(source="microphone", type="filepath"), #streaming = True, # # "state" # ], # outputs=[ # "textbox", "textbox", "textbox", "textbox", "audio", # ], # live=True).launch()