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)


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()