Upload app.py

app.py

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+import librosa
+import numpy as np
+import torch
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+import gradio as gr
+import librosa
+import numpy as np
+import torch
+
+checkpoint = "microsoft/speecht5_tts"
+processor = SpeechT5Processor.from_pretrained(checkpoint)
+model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+def predict(text):
+    if len(text.strip()) == 0:
+        return (16000, np.zeros(0).astype(np.int16))
+    inputs = processor(text=text, return_tensors="pt")
+    # limit input length
+    input_ids = inputs["input_ids"]
+    input_ids = input_ids[..., :model.config.max_text_positions]
+    speaker_embedding = np.load("cmu_us_ksp_arctic-wav-arctic_b0087.npy")
+    speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
+    speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder)
+    speech = (speech.numpy() * 32767).astype(np.int16)
+    return (16000, speech)
+
+gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Text(label="Input Text"),
+        gr.Radio(label="Speaker", choices=[
+            "KSP (male)"
+        ],
+        value="KSP (male)"),
+    ],
+    outputs=[
+        gr.Audio(label="Generated Speech", type="numpy"),
+    ],
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+).launch()