Jaikinator
158 lines
5.2 KiB
Python
158 lines
5.2 KiB
Python
"""
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stg - scraibe to gradio interface
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This file contains the code for the scraibe to gradio interface.
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It makes adds gradio interactions to the scraibe class in the back.
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"""
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import json
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import gradio as gr
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from tqdm import tqdm
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from scraibe import Scraibe
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class GradioTranscriptionInterface:
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"""
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Interface handling the interaction between Gradio UI and the Audio Transcription system.
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"""
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def __init__(self, model: Scraibe):
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"""
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Initializes the GradioTranscriptionInterface with a transcription model.
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Args:
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model (Scraibe): Model responsible for audio transcription tasks.
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"""
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self.model = model
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def auto_transcribe(self, source,
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num_speakers : int,
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translation : bool,
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language : str):
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"""
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Shortcut method for the Scraibe task.
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Returns:
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tuple: Transcribed text (str), JSON output (dict)
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"""
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kwargs = {
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"num_speakers": num_speakers if num_speakers != 0 else None,
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"language": language if language != "None" else None,
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"task": 'translate' if translation else None
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}
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if isinstance(source, str):
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try:
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result = self.model.autotranscribe(source, **kwargs)
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except ValueError:
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raise gr.Error("Couldn't detect any speech in the provided audio. \
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Please try again!")
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return str(result), result.get_json()
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elif isinstance(source, list):
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source_names = [s.split("/")[-1] for s in source]
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result = []
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for s in tqdm(source, total=len(source),desc = "Transcribing audio files"):
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try:
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res = self.model.autotranscribe(s, **kwargs)
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except ValueError:
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_name = s.split("/")[-1]
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res = f"NO TRANSCRIPT FOUND FOR {_name}"
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gr.Warning(f"Couldn't detect any speech in {_name} will skip this file.")
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result.append(res)
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out = ''
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out_dict = {}
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for i, r in enumerate(result):
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out += f"TRANSCRIPT FOR {source_names[i]}:\n\n"
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out += str(r)
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out += "\n\n"
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if isinstance(r, str):
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out_dict[source_names[i]] = r
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else:
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out_dict[source_names[i]] = r.get_dict()
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return out, json.dumps(out_dict, indent=4)
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else:
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raise gr.Error("Please provide a valid audio file.")
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def transcribe(self, source, translation, language):
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"""
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Shortcut method for the Transcribe task.
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Returns:
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str: Transcribed text.
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"""
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kwargs = {
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"language": language if language != "None" else None,
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"task": 'translate' if translation == "Yes" else None
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}
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if isinstance(source, str):
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result = self.model.transcribe(source, **kwargs)
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return str(result)
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elif isinstance(source, list):
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source_names = [s.split("/")[-1] for s in source]
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result = []
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for s in tqdm(source, total=len(source),desc = "Transcribing audio files"):
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res = self.model.transcribe(s, **kwargs)
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result.append(res)
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out = ''
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for i, res in enumerate(result):
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out += f"TRANSCRIPT FOR {source_names[i]}:\n\n"
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out += str(res)
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out += "\n\n"
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return out
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else:
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raise gr.Error("Please provide a valid audio file.")
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def perform_diarisation(self, source, num_speakers):
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"""
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Shortcut method for the Diarisation task.
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Returns:
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str: JSON output of diarisation result.
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"""
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kwargs = {
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"num_speakers": num_speakers if num_speakers != 0 else None,
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}
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if isinstance(source, str):
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try:
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result = self.model.diarization(source, **kwargs)
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except ValueError:
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raise gr.Error("Couldn't detect any speech in the provided audio. \
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Please try again!")
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return json.dumps(result, indent=2)
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elif isinstance(source, list):
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source_names = [s.split("/")[-1] for s in source]
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result = []
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for s in tqdm(source, total=len(source),desc = "Performing diarisation"):
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try:
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res = self.model.diarization(s, **kwargs)
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except ValueError:
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res = f"NO DIARISATION FOUND FOR {s}"
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gr.Warning(f"Couldn't detect any speech in {s} will skip this file.")
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result.append(res)
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out = {}
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for i, res in enumerate(result):
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out[source_names[i]] = res
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return json.dumps(out, indent=4)
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else:
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gr.Error("Please provide a valid audio file.")
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