Create App2.py

App2.py

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+import gradio as gr
+import librosa
+import numpy as np
+import torch
+import os
+import torch
+from speechbrain.pretrained import EncoderClassifier
+from scipy.io import wavfile
+from IPython.display import Audio
+from transformers import AutoProcessor, AutoModelForTextToSpectrogram, SpeechT5HifiGan
+
+processor = AutoProcessor.from_pretrained("Prasada/speecht5_tts_voxpopuli_nl")
+model = AutoModelForTextToSpectrogram.from_pretrained("Prasada/speecht5_tts_voxpopuli_nl")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+
+spk_model_name = "speechbrain/spkrec-xvect-voxceleb"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+speaker_model = EncoderClassifier.from_hparams(
+    source=spk_model_name,
+    run_opts={"device": device},
+    savedir=os.path.join("/tmp", spk_model_name))
+
+def create_speaker_embedding(waveform):
+    with torch.no_grad():
+        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
+        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
+        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
+    return speaker_embeddings
+
+def prepare_data(temp_text, temp_audio):
+    rate, audio_data = wavfile.read(temp_audio)
+    example = processor(
+        text=temp_text,
+        audio_target=audio_data,
+        sampling_rate=16000,
+        return_attention_mask=False,)
+    example["speaker_embeddings"] = create_speaker_embedding(audio_data)
+    example_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
+    return example_embeddings
+
+
+def predict(temp_text, temp_audio, text):
+    text = text
+    embeddings=prepare_data(temp_text, temp_audio)
+    inputs = processor(text=text, return_tensors="pt")
+    spectrogram = model.generate_speech(inputs["input_ids"], embeddings)
+
+    with torch.no_grad():
+        speech = vocoder(spectrogram)
+ 
+    return Audio(speech.numpy(), rate=16000)
+
+
+gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Text(label="Template Text"),
+        gr.Audio(label="Template Speech", type="numpy"),
+        gr.Text(label="Input Text"),
+    ],
+    outputs=[
+        gr.Audio(label="Generated Speech", type="numpy"),
+    ],
+
+).launch()