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| import os | |
| import gradio as gr | |
| import whisper | |
| import requests | |
| import tempfile | |
| from neon_tts_plugin_coqui import CoquiTTS | |
| from datasets import load_dataset | |
| dataset = load_dataset("ysharma/short_jokes") | |
| # Language common in both the multilingual models - English, Chinese, Spanish, and French etc | |
| # 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", | |
| # 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 - English, 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(transcribe, lang) | |
| else: | |
| speech = tts(translation, 'en') #'en') | |
| return transcribe, translation, 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 | |
| # 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("<h1><center>Multilingual AI Assistant - Voice to Joke</center></h1>") | |
| 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>- Front end is built using [**Gradio Block API**](https://gradio.app/docs/#blocks).<br>Both CoquiTTS and Whisper are Multilingual, there are several overlapping languages between them. Hence it would be suggested to test this ML-App using these two languages to get the best results</u>.<br>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! | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| in_audio = gr.Audio(source="microphone", type="filepath", label='Record your voice command here in English, Spanish or French for best results-') #type='filepath' | |
| b1 = gr.Button("AI response pipeline (Whisper - Bloom - Coqui pipeline)") | |
| out_transcript = gr.Textbox(label= 'English/Spanish/French Transcript of your Audio 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 language - This will be either in Spanish, or in French or in English for all other languages -') | |
| 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) |