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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() | |