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import gradio as gr
from aip_trainer import app_logger
from aip_trainer.lambdas import lambdaSpeechToScore, lambdaTTS
js = """
function updateCssText(text, letters) {
let wordsArr = text.split(" ")
let lettersWordsArr = letters.split(" ")
let speechOutputContainer = document.querySelector('#speech-output');
speechOutputContainer.textContent = ""
for (let idx in wordsArr) {
let word = wordsArr[idx]
let letterIsCorrect = lettersWordsArr[idx]
for (let idx1 in word) {
let letterCorrect = letterIsCorrect[idx1] == "1"
let containerLetter = document.createElement("span")
containerLetter.style.color = letterCorrect ? 'green' : "red"
containerLetter.innerText = word[idx1];
speechOutputContainer.appendChild(containerLetter)
}
let containerSpace = document.createElement("span")
containerSpace.textContent = " "
speechOutputContainer.appendChild(containerSpace)
}
}
"""
with gr.Blocks() as gradio_app:
app_logger.info("start gradio app building...")
gr.Markdown(
"""
# AI Pronunciation Trainer
See [my fork](https://github.com/trincadev/ai-pronunciation-trainer) of [AI Pronunciation Trainer](https://github.com/Thiagohgl/ai-pronunciation-trainer) repositroy
for more details.
"""
)
with gr.Row():
with gr.Column(scale=4, min_width=300):
with gr.Row():
with gr.Column(scale=1, min_width=50):
language = gr.Radio(["de", "en"], label="Language", value="en")
with gr.Column(scale=7, min_width=300):
learner_transcription = gr.Textbox(
lines=3,
label="Learner Transcription",
value="Hi there, how are you?",
)
with gr.Row():
learner_recording = gr.Audio(
label="Learner Recording",
sources=["microphone", "upload"],
type="filepath",
)
with gr.Row():
tts = gr.Audio(label="tts")
btn = gr.Button(value="TTS")
btn.click(
fn=lambdaTTS.get_tts,
inputs=[learner_transcription, language],
outputs=tts,
)
gr.Examples(
examples=[
["Hi there, how are you?", "en"],
["Hallo, wie geht es dir?", "de"],
],
inputs=[learner_transcription, language],
)
with gr.Column(scale=3, min_width=300):
transcripted_text = gr.Textbox(
lines=2, placeholder=None, label="Transcripted text", visible=False
)
letter_correctness = gr.Textbox(
lines=1,
placeholder=None,
label="Letters correctness",
visible=False,
)
pronunciation_accuracy = gr.Textbox(
lines=1, placeholder=None, label="Pronunciation accuracy %"
)
recording_ipa = gr.Textbox(
lines=1, placeholder=None, label="Learner phonetic transcription"
)
ideal_ipa = gr.Textbox(
lines=1, placeholder=None, label="Ideal phonetic transcription"
)
res = gr.Textbox(lines=1, placeholder=None, label="RES", visible=False)
html_output = gr.HTML(
label="Speech accuracy output",
elem_id="speech-output",
show_label=True,
visible=True,
render=True,
value=" - ",
elem_classes="speech-output",
)
btn = gr.Button(value="Recognize speech accuracy")
# real_transcripts, is_letter_correct_all_words, pronunciation_accuracy, result['recording_ipa'], real_transcripts_ipa, res
btn.click(
lambdaSpeechToScore.get_speech_to_score_tuple,
inputs=[learner_transcription, learner_recording, language],
outputs=[
transcripted_text,
letter_correctness,
pronunciation_accuracy,
recording_ipa,
ideal_ipa,
res,
],
)
html_output.change(
None,
inputs=[transcripted_text, letter_correctness],
outputs=[html_output],
js=js,
)
if __name__ == "__main__":
gradio_app.launch()
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