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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 whisper | |
dataset = load_dataset("ysharma/short_jokes") | |
# 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_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", | |
#Model 2: 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) : | |
translation, lang = whisper_stt(audio) # older : transcribe, translation, lang | |
#text1 = model.transcribe(audio)["text"] | |
#if translation | |
#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 translation, speech #transcribe, | |
# 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, mel, options_transl) #model_med | |
# print the recognized text | |
#print(f"transcript is : {result_transc.text}") | |
print(f"translation is : {result_transl.text}") | |
return result_transl.text, lang #result_transc.text, | |
# 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>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><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 -') #type='filepath' | |
b1 = gr.Button("AI Response") | |
out_transcript = gr.Textbox(label= '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='Audio response form CoquiTTS') | |
#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_audio]) #out_translation_en, out_generated_text,out_generated_text_en, | |
demo.launch(enable_queue=True, debug=True) |