| 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_generate_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_generate, text_generate_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}") | |
| # decode the audio | |
| #options = whisper.DecodingOptions(fp16 = False, language='en') #lang | |
| #result = whisper.decode(model, mel, options) | |
| # print the recognized text | |
| # print(f"transcript is : {result.text}") | |
| # return result.text, lang | |
| 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 = """Question: How are you doing today? | |
| Answer: I am doing good, thanks. | |
| 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 + prompt + "\n" + "Answer: " | |
| #else: | |
| 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: | |
| 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("<h1><center>Testing</center></h1>") | |
| 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() | |