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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 getNews ():
return requests.get ("https://newsapi.org/v2/top-headlines?country=us&apiKey=3bca07c913ec4703a23f6ba03e15b30b").content
def predict(text, speaker):
if len(text.strip()) == 0:
return (16000, np.zeros(0).astype(np.int16))
inputs = processor(text=getNews(), 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)
title = "SpeechT5: Speech Synthesis"
description = """
The <b>SpeechT5</b> model is pre-trained on text as well as speech inputs, with targets that are also a mix of text and speech.
By pre-training on text and speech at the same time, it learns unified representations for both, resulting in improved modeling capabilities.
"""
article = """
<div style='margin:20px auto;'>
<p>References: <a href="https://arxiv.org/abs/2110.07205">SpeechT5 paper</a> |
<a href="https://github.com/microsoft/SpeechT5/">original GitHub</a> |
<a href="https://huggingface.co/mechanicalsea/speecht5-tts">original weights</a></p>
<p>Speaker embeddings were generated from <a href="http://www.festvox.org/cmu_arctic/">CMU ARCTIC</a> using <a href="https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py">this script</a>.</p>
</div>
"""
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()
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