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| import gradio as gr | |
| from transformers import pipeline | |
| from datasets import load_dataset | |
| import soundfile as sf | |
| import torch | |
| import os | |
| import io | |
| import base64 | |
| import numpy as np | |
| from pydub import AudioSegment | |
| os.environ['TRANSFORMERS_CACHE'] = '.cache' | |
| print ("----- setting up pipeline -----") | |
| synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts") | |
| print ("----- setting up dataset -----") | |
| embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) | |
| # You can replace this embedding with your own as well. | |
| print ("----- synthetizing audio -----") | |
| #speech = synthesiser("Hello, my dog is cooler than you!", forward_params={"speaker_embeddings": speaker_embedding}) | |
| #sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"]) | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| def synthesise_audio(text, forward_params=None): | |
| if len(text) > 100: | |
| raise ValueError("Error: El texto es demasiado largo. Por favor, limita tu entrada a 100 caracteres.") | |
| speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding}) | |
| # sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"]) | |
| # return "speech.wav" | |
| # Convert numpy array to audio | |
| #with io.BytesIO() as f: | |
| # sf.write(f, speech["audio"], samplerate=speech["sampling_rate"], format='wav') | |
| # audio = f.getvalue() | |
| # Convert numpy array to audio | |
| audio = np.int16(speech["audio"] * 32767) | |
| audio_segment = AudioSegment(audio, sample_width=2, frame_rate=speech["sampling_rate"], channels=1) | |
| #return speech["audio"] | |
| return audio | |
| #demo = gr.Interface(fn=greet, inputs="text", outputs="text", description="----- TTS Testing -----") | |
| input_text = gr.Textbox(lines=10, label="Enter text here") | |
| demo = gr.Interface(fn=synthesise_audio, | |
| inputs=input_text, | |
| #outputs="audio", | |
| outputs = gr.Audio(type="numpy"), | |
| description="----- manuai Text To Speech generator -----", | |
| allow_flagging = False) | |
| demo.launch(debug = True) | |