Create app.py

app.py

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+import gradio as gr
+import librosa
+import numpy as np
+import torch
+
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+
+
+checkpoint = "microsoft/speecht5_tts"
+processor = SpeechT5Processor.from_pretrained(checkpoint)
+model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+
+speaker_embeddings = {
+    "BDL": "spkemb/cmu_us_bdl_arctic-wav-arctic_a0009.npy",
+    "CLB": "spkemb/cmu_us_clb_arctic-wav-arctic_a0144.npy",
+    "KSP": "spkemb/cmu_us_ksp_arctic-wav-arctic_b0087.npy",
+    "RMS": "spkemb/cmu_us_rms_arctic-wav-arctic_b0353.npy",
+    "SLT": "spkemb/cmu_us_slt_arctic-wav-arctic_a0508.npy",
+}
+
+
+def predict(text, speaker):
+    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]
+
+    if speaker == "Surprise Me!":
+        # load one of the provided speaker embeddings at random
+        idx = np.random.randint(len(speaker_embeddings))
+        key = list(speaker_embeddings.keys())[idx]
+        speaker_embedding = np.load(speaker_embeddings[key])
+
+        # randomly shuffle the elements
+        np.random.shuffle(speaker_embedding)
+
+        # randomly flip half the values
+        x = (np.random.rand(512) >= 0.5) * 1.0
+        x[x == 0] = -1.0
+        speaker_embedding *= x
+
+        #speaker_embedding = np.random.rand(512).astype(np.float32) * 0.3 - 0.15
+    else:
+        speaker_embedding = np.load(speaker_embeddings[speaker[:3]])
+
+    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)
+
+examples = [
+    ["It is not in the stars to hold our destiny but in ourselves.", "BDL (male)"],
+    ["The octopus and Oliver went to the opera in October.", "CLB (female)"],
+    ["She sells seashells by the seashore. I saw a kitten eating chicken in the kitchen.", "RMS (male)"],
+    ["Brisk brave brigadiers brandished broad bright blades, blunderbusses, and bludgeons—balancing them badly.", "SLT (female)"],
+    ["A synonym for cinnamon is a cinnamon synonym.", "BDL (male)"],
+    ["How much wood would a woodchuck chuck if a woodchuck could chuck wood? He would chuck, he would, as much as he could, and chuck as much wood as a woodchuck would if a woodchuck could chuck wood.", "CLB (female)"],
+]
+
+gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Text(label="Input Text"),
+        gr.Radio(label="Speaker", choices=[
+            "BDL (male)",
+            "CLB (female)",
+            "KSP (male)",
+            "RMS (male)",
+            "SLT (female)",
+            "Surprise Me!"
+        ],
+        value="BDL (male)"),
+    ],
+    outputs=[
+        gr.Audio(label="Generated Speech", type="numpy"),
+    ],
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+).launch()