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Spanish-Audio-Transcription-based-Harassment-Detection

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Spanish-Audio-Transcription-based-Harassment-Detection / app.py

DrishtiSharma

Update app.py

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	#importing all the required libraries
	import gradio as gr
	import librosa
	from transformers import AutoFeatureExtractor, pipeline

	#Loading and fixing the audio input
	def load_and_fix_data(input_file, model_sampling_rate):
	speech, sample_rate = librosa.load(input_file)
	if len(speech.shape) > 1:
	speech = speech[:, 0] + speech[:, 1]
	if sample_rate != model_sampling_rate:
	speech = librosa.resample(speech, sample_rate, model_sampling_rate)
	return speech

	#Loading the feature extractor and instantiating the pipeline
	model_name1 = "jonatasgrosman/wav2vec2-xls-r-1b-spanish"
	feature_extractor = AutoFeatureExtractor.from_pretrained(model_name1)
	sampling_rate = feature_extractor.sampling_rate
	asr = pipeline("automatic-speech-recognition", model=model_name1)

	#Instantiating a pipeline for harassment detection (text classification)
	model_name2 = "hackathon-pln-es/Detect-Acoso-Twitter-Es"
	classifier = pipeline("text-classification", model = model_name2)

	#Defining a function for speech-to-text-conversion
	def speech_to_text(input_file):
	speech = load_and_fix_data(input_file, sampling_rate)
	transcribed_text = asr(speech, chunk_length_s=15, stride_length_s=1)["text"]
	return transcribed_text

	#Defining a function for Harassment detection (text classification)
	def harassment_detector(transcribed_text):
	harassment_detection = classifier(transcribed_text)[0]["label"]
	return harassment_detection

	#Defining a function which outputs audio transcription and the output of harassment detection module
	new_line = "\n\n\n"
	def asr_and_harassment_detection(input_file):
	transcribed_text = speech_to_text(input_file)
	harassment_detection = harassment_detector(transcribed_text)
	return f"Audio Transcription :{transcribed_text} {new_line} Audio content is: {harassment_detection}"


	inputs=[gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")]
	outputs=[gr.outputs.Textbox(label="Predicción")]
	examples=[["audio2.wav"], ["sample_audio.wav"], ["test1.wav"], ["test2.wav"]]
	title="Spanish-Audio-Transcription-based-Harassment-Detection"

	description = """ This is a Gradio demo for Spanish audio transcription-based harassment detection. To use this, simply provide an audio input (audio recording or via microphone), which will subsequently be transcribed and classified as Harassment/non-harassment pertaining to audio (transcription) with the help of pre-trained models.


	Pre-trained model used for Spanish ASR: [jonatasgrosman/wav2vec2-xls-r-1b-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-spanish)


	Pre-trained model used for Harassment Detection: [hackathon-pln-es/Detect-Acoso-Twitter-Es](https://huggingface.co/hackathon-pln-es/Detect-Acoso-Twitter-Es)"""

	gr.Interface(
	asr_and_harassment_detection,
	inputs=inputs,
	outputs=outputs,
	examples=examples,
	title=title,
	description=description,
	layout="horizontal",
	theme="huggingface",
	).launch(enable_queue=True)