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import csv
from pathlib import Path
from typing import Tuple
import gradio as gr
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from whisper_bidec import decode_wav, get_logits_processor, load_corpus_from_sentences
def _parse_file(file_path: str) -> list[str]:
"""Parse .txt / .md / .csv and return its content as a list of strings by splitting per new line or row."""
if file_path.endswith(".csv"):
sentences = []
with open(file_path, "r", encoding="utf-8") as f:
reader = csv.reader(f)
for row in reader:
sentences.append(row)
else:
with open(file_path, "r") as f:
sentences = f.readlines()
return sentences
def transcribe(
processor_name: str,
audio: str,
bias_strength: float,
bias_text: str | None,
bias_text_file: str | None,
) -> Tuple[str, str]:
processor = WhisperProcessor.from_pretrained(processor_name)
model = WhisperForConditionalGeneration.from_pretrained(processor_name)
sentences = ""
if bias_text:
sentences = bias_text.split(",")
elif Path(bias_text_file).is_file():
sentences = _parse_file(bias_text_file)
if sentences:
corpus = load_corpus_from_sentences(sentences, processor)
logits_processor = get_logits_processor(
corpus=corpus, processor=processor, bias_towards_lm=bias_strength
)
text_with_bias = decode_wav(
model, processor, audio, logits_processor=logits_processor
)
else:
text_with_bias = ""
text_no_bias = decode_wav(model, processor, audio, logits_processor=None)
return text_no_bias, text_with_bias
def setup_gradio_demo():
css = """
#centered-column {
display: flex;
justify-content: center;
align-items: center;
flex-direction: column;
text-align: center;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown("# Whisper Bidec Demo")
gr.Markdown("## Step 1: Select a Whisper model")
processor = gr.Textbox(
value="openai/whisper-tiny.en", label="Whisper Model from Hugging Face"
)
gr.Markdown("## Step 2: Upload your audio file")
audio_clip = gr.Audio(type="filepath", label="Upload a WAV file")
gr.Markdown("## Step 3: Set your biasing text")
with gr.Row():
with gr.Column(scale=20):
gr.Markdown(
"You can add multiple possible sentences by separating them with a comma <,>."
)
bias_text = gr.Textbox(label="Write your biasing text here")
with gr.Column(scale=1, elem_id="centered-column"):
gr.Markdown("## OR")
with gr.Column(scale=20):
gr.Markdown(
"Note that each new line (.txt / .md) or row (.csv) will be treated as a separate sentence to bias towards to."
)
bias_text_file = gr.File(
label="Upload a file with multiple lines of text",
file_types=[".txt", ".md", ".csv"],
)
gr.Markdown("## Step 4: Set how much you want to bias towards the LM")
bias_amount = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.5,
step=0.1,
label="Bias strength",
interactive=True,
)
gr.Markdown("## Step 5: Get your transcription before and after biasing")
transcribe_button = gr.Button("Transcribe")
with gr.Row():
with gr.Column():
output = gr.Text(label="Output")
with gr.Column():
biased_output = gr.Text(label="Biased output")
transcribe_button.click(
fn=transcribe,
inputs=[
processor,
audio_clip,
bias_amount,
bias_text,
bias_text_file,
],
outputs=[output, biased_output],
)
demo.launch()
if __name__ == "__main__":
setup_gradio_demo()
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