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| import os | |
| #import numpy as np | |
| import gradio as gr | |
| import whisper | |
| import requests | |
| import tempfile | |
| from neon_tts_plugin_coqui import CoquiTTS | |
| # Whisper: Speech-to-text | |
| model = whisper.load_model("base") | |
| # The LLM : Bloom | |
| API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" | |
| HF_TOKEN = os.environ["HF_TOKEN"] | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| #en, fr, esp, arb, hn, portu, Indonesian, Vietnamese, Chinese, tamil, telugu, bengali | |
| # Text-to-Speech | |
| LANGUAGES = list(CoquiTTS.langs.keys()) | |
| print(f"Languages for Coqui are: {LANGUAGES}") | |
| coquiTTS = CoquiTTS() | |
| def whisper_stt(audio): | |
| # 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) | |
| print(f"Detected language: {max(probs, key=probs.get)}") | |
| # decode the audio | |
| options = whisper.DecodingOptions() | |
| result = whisper.decode(model, mel, options) | |
| # print the recognized text | |
| print(f"transcript is : {result.text}") | |
| return result.text | |
| # Processing input Audio | |
| def fun_engine(audio) : | |
| text1 = whisper_stt(audio) | |
| #text1 = model.transcribe(audio)["text"] | |
| text2 = lang_model_response(text1) | |
| speech = tts(text, 'en') | |
| return text1, text2, speech | |
| def lang_model_response(prompt): | |
| print(f"*****Inside meme_generate - Prompt is :{prompt}") | |
| if len(prompt) == 0: | |
| prompt = """Can you help me please?""" | |
| json_ = {"inputs": prompt, | |
| "parameters": | |
| { | |
| "top_p": 0.90, #0.90 default | |
| "max_new_tokens": 64, | |
| "temperature": 1.1, #1.1 default | |
| "return_full_text": True, | |
| "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() | |
| print(f"output is : {output}") | |
| output_tmp = output[0]['generated_text'] | |
| print(f"output_tmp is: {output_tmp}") | |
| solution = output_tmp.split(".")[1] | |
| print(f"Final response after splits is: {solution}") | |
| return solution | |
| #Text-to-Speech | |
| def tts(text, language): | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: | |
| coquiTTS.get_tts(text, fp, speaker = {"language" : language}) | |
| return fp.name | |
| #demo = gr.Interface(fn=tts, inputs=inputs, outputs=outputs) | |
| demo.launch() | |
| gr.Interface( | |
| title = 'Testing Whisper', | |
| fn=fun_engine, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath"), #streaming = True, | |
| # "state" | |
| ], | |
| outputs=[ | |
| "textbox", "textbox", "audio", | |
| ], | |
| live=True).launch() | |