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| import os | |
| import shutil | |
| from pathlib import Path | |
| import gradio as gr | |
| import pandas as pd | |
| from datasets import Audio, Dataset | |
| from speechline.segmenters import SilenceSegmenter, WordOverlapSegmenter | |
| from speechline.transcribers import Wav2Vec2Transcriber | |
| from speechline.utils.tokenizer import WordTokenizer | |
| MAX_SEGMENTS = 100 | |
| OUTPUT_DIR = "tmp" | |
| def segmentation_interface(choice: str): | |
| if choice == "Silence Gap": | |
| return gr.update(visible=True), gr.update(visible=False) | |
| elif choice == "Word Overlap": | |
| return gr.update(visible=False), gr.update(visible=True) | |
| def run(audio_path, model, segmentation_type, silence_duration, ground_truth): | |
| transcriber = Wav2Vec2Transcriber(model) | |
| dataset = Dataset.from_dict({"audio": [audio_path]}) | |
| dataset = dataset.cast_column( | |
| "audio", Audio(sampling_rate=transcriber.sampling_rate) | |
| ) | |
| output_offsets = transcriber.predict(dataset, output_offsets=True) | |
| if segmentation_type == "Silence Gap": | |
| segmenter = SilenceSegmenter() | |
| elif segmentation_type == "Word Overlap": | |
| segmenter = WordOverlapSegmenter() | |
| tokenizer = WordTokenizer() | |
| if os.path.exists(OUTPUT_DIR): | |
| shutil.rmtree(OUTPUT_DIR) | |
| segmenter.chunk_audio_segments( | |
| audio_path, | |
| OUTPUT_DIR, | |
| output_offsets[0], | |
| minimum_chunk_duration=0, | |
| silence_duration=silence_duration, | |
| ground_truth=tokenizer(ground_truth), | |
| ) | |
| outputs, idx = [], 0 | |
| for path in sorted(Path(OUTPUT_DIR).rglob("*")): | |
| if path.suffix == ".tsv": | |
| gt = pd.read_csv( | |
| path, sep="\t", names=["start_offset", "end_offset", "text"] | |
| ) | |
| outputs.append(gr.Dataframe.update(value=gt, visible=True)) | |
| elif path.suffix == ".wav": | |
| outputs.append(gr.Audio.update(value=str(path), visible=True)) | |
| idx += 1 | |
| for _ in range(MAX_SEGMENTS - idx): | |
| outputs += [gr.Dataframe.update(visible=False), gr.Audio.update(visible=False)] | |
| return outputs | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| f""" | |
| <center> | |
| # 🎙️ SpeechLine Demo | |
| [Repository](https://github.com/bookbot-kids/speechline) | [Documentation](https://bookbot-kids.github.io/speechline/) | |
| </center> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio = gr.Audio(type="filepath") | |
| model = gr.Dropdown( | |
| choices=[ | |
| "facebook/wav2vec2-base-960h", | |
| ], | |
| value="facebook/wav2vec2-base-960h", | |
| label="Transcriber Model", | |
| ) | |
| segmenter = gr.Radio( | |
| choices=["Silence Gap", "Word Overlap"], | |
| value="Silence Gap", | |
| label="Segmentation Method", | |
| ) | |
| sil = gr.Slider( | |
| 0, 1, value=0.1, step=0.1, label="Silence Duration", visible=True | |
| ) | |
| gt = gr.Textbox( | |
| label="Ground Truth", | |
| placeholder="Enter Ground Truth Text", | |
| interactive=True, | |
| visible=False, | |
| ) | |
| segmenter.change( | |
| fn=segmentation_interface, inputs=segmenter, outputs=[sil, gt] | |
| ) | |
| inputs = [audio, model, segmenter, sil, gt] | |
| transcribe_btn = gr.Button("Transcribe") | |
| with gr.Column(): | |
| outputs = [ | |
| gr.Dataframe( | |
| visible=True, headers=["start_offset", "end_offset", "text"] | |
| ), | |
| gr.Audio(visible=True), | |
| ] | |
| for _ in range(MAX_SEGMENTS - 1): | |
| outputs += [gr.Dataframe(visible=False), gr.Audio(visible=False)] | |
| transcribe_btn.click(fn=run, inputs=inputs, outputs=outputs) | |
| demo.launch() | |