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from pathlib import Path
import tempfile
import gradio as gr
import librosa
import tgt.core
import tgt.io3
from transformers import pipeline
TEXTGRID_DIR = tempfile.mkdtemp()
DEFAULT_MODEL = "ginic/data_seed_bs64_4_wav2vec2-large-xlsr-53-buckeye-ipa"
TEXTGRID_DOWNLOAD_TEXT = "Download TextGrid file"
TEXTGRID_NAME_INPUT_LABEL = "TextGrid file name"
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",
"ginic/vary_individuals_old_only_1_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/vary_individuals_old_only_2_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/vary_individuals_old_only_3_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/vary_individuals_young_only_1_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/vary_individuals_young_only_2_wav2vec2-large-xlsr-53-buckeye-ipa",
"ginic/vary_individuals_young_only_3_wav2vec2-large-xlsr-53-buckeye-ipa",
]
def load_model_and_predict(
model_name: str,
audio_in: str,
model_state: dict,
):
if audio_in is None:
return (
"",
model_state,
gr.Textbox(label=TEXTGRID_NAME_INPUT_LABEL, interactive=False),
)
if model_state["model_name"] != model_name:
model_state = {
"loaded_model": pipeline(
task="automatic-speech-recognition", model=model_name
),
"model_name": model_name,
}
prediction = model_state["loaded_model"](audio_in)["text"]
return (
prediction,
model_state,
gr.Textbox(
label=TEXTGRID_NAME_INPUT_LABEL,
interactive=True,
value=Path(audio_in).with_suffix(".TextGrid").name,
),
)
def get_textgrid_contents(audio_in, textgrid_tier_name, transcription_prediction):
if audio_in is None or transcription_prediction is None:
return ""
duration = librosa.get_duration(path=audio_in)
annotation = tgt.core.Interval(0, duration, transcription_prediction)
transcription_tier = tgt.core.IntervalTier(
start_time=0, end_time=duration, name=textgrid_tier_name
)
transcription_tier.add_annotation(annotation)
textgrid = tgt.core.TextGrid()
textgrid.add_tier(transcription_tier)
return tgt.io3.export_to_long_textgrid(textgrid)
def write_textgrid(textgrid_contents, textgrid_filename):
"""Writes the text grid contents to a named file in the temporary directory.
Returns the path for download.
"""
textgrid_path = Path(TEXTGRID_DIR) / Path(textgrid_filename).name
textgrid_path.write_text(textgrid_contents)
return textgrid_path
def get_interactive_download_button(textgrid_contents, textgrid_filename):
return gr.DownloadButton(
label=TEXTGRID_DOWNLOAD_TEXT,
variant="primary",
interactive=True,
value=write_textgrid(textgrid_contents, textgrid_filename),
)
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")
gr.Markdown("""## TextGrid File Options
Change these inputs if you'd like to customize and download the transcription in [TextGrid format](https://www.fon.hum.uva.nl/praat/manual/TextGrid_file_formats.html) for Praat.
""")
textgrid_tier = gr.Textbox(
label="TextGrid Tier Name", value="transcription", interactive=True
)
textgrid_filename = gr.Textbox(
label=TEXTGRID_NAME_INPUT_LABEL, interactive=False
)
textgrid_contents = gr.Textbox(
label="TextGrid Contents",
value=get_textgrid_contents,
inputs=[audio_in, textgrid_tier, prediction],
)
download_btn = gr.DownloadButton(
label=TEXTGRID_DOWNLOAD_TEXT,
interactive=False, # Don't allow download button to be active until an upload happened
variant="primary",
)
# Update prediction if model or audio changes
gr.on(
triggers=[audio_in.input, model_name.change],
fn=load_model_and_predict,
inputs=[model_name, audio_in, model_state],
outputs=[prediction, model_state, textgrid_filename],
)
# Download button becomes interactive if user updates audio or textgrid params
gr.on(
triggers=[textgrid_contents.change, textgrid_filename.change],
fn=get_interactive_download_button,
inputs=[textgrid_contents, textgrid_filename],
outputs=[download_btn],
)
demo.launch(max_file_size="100mb")
if __name__ == "__main__":
launch_demo()
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