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from pathlib import Path
import gradio as gr
from transformers import pipeline
DEFAULT_MODEL = "ginic/data_seed_bs64_4_wav2vec2-large-xlsr-53-buckeye-ipa"
VALID_MODELS = [
"ctaguchi/wav2vec2-large-xlsr-japlmthufielta-ipa-plus-2000",
"ginic/data_seed_bs64_1_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/data_seed_bs64_2_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/data_seed_bs64_3_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/data_seed_bs64_4_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_30_female_1_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_30_female_2_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_30_female_3_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_30_female_4_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_30_female_5_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_70_female_1_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_70_female_2_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_70_female_3_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_70_female_4_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/gender_split_70_female_5_wav2vec2-large-xlsr-53-buckeye-ipa",
]
def load_model_and_predict(model_name: str, audio_in: str, model_state: dict):
if model_state["model_name"] != model_name:
model_state = {
"loaded_model": pipeline(
task="automatic-speech-recognition", model=model_name
),
"model_name": model_name,
}
return (
model_state["loaded_model"](audio_in)["text"],
model_state,
gr.DownloadButton("Download TextGrid file", visible=True),
)
def download_textgrid(audio_in, textgrid_tier_name, prediction):
# TODO
pass
def launch_demo():
initial_model = {
"loaded_model": pipeline(
task="automatic-speech-recognition", model=DEFAULT_MODEL
),
"model_name": DEFAULT_MODEL,
}
with gr.Blocks() as demo:
gr.Markdown(
"""# Automatic International Phonetic Alphabet Transcription
This demo allows you to experiment with producing phonetic transcriptions of uploaded or recorded audio using a selected automatic speech recognition (ASR) model.""",
)
model_name = gr.Dropdown(
VALID_MODELS,
value=DEFAULT_MODEL,
label="IPA transcription ASR model",
info="Select the model to use for prediction.",
)
audio_in = gr.Audio(type="filepath", show_download_button=True)
model_state = gr.State(value=initial_model)
prediction = gr.Textbox(label="Predicted IPA transcription")
textgrid_tier = gr.Textbox(
label="TextGrid Tier Name", value="transcription", interactive=True
)
download_btn = gr.DownloadButton("Download TextGrid file", visible=False)
# If user updates model name or audio, run prediction
audio_in.input(
fn=load_model_and_predict,
inputs=[model_name, audio_in, model_state],
outputs=[prediction, model_state, download_btn],
)
model_name.change(
fn=load_model_and_predict,
inputs=[model_name, audio_in, model_state],
outputs=[prediction, model_state, download_btn],
)
# demo = gr.Interface(
# fn=load_model_and_predict,
# inputs=[
# gr.Dropdown(
# VALID_MODELS,
# value=DEFAULT_MODEL,
# label="IPA transcription ASR model",
# info="Select the model to use for prediction.",
# ),
# gr.Audio(type="filepath", show_download_button=True),
# gr.State(
# value=initial_model
# ), # Store the name of the currently loaded model
# ],
# outputs=[gr.Textbox(label="Predicted IPA transcription"), gr.State()],
# allow_flagging="never",
# title="Automatic International Phonetic Alphabet Transcription",
# description="This demo allows you to experiment with producing phonetic transcriptions of uploaded or recorded audio using a selected automatic speech recognition (ASR) model.",
# )
demo.launch()
if __name__ == "__main__":
launch_demo()
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