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import gradio as gr |
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import librosa |
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from transformers import AutoFeatureExtractor, AutoModelForSeq2SeqLM, AutoTokenizer, pipeline |
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def load_and_fix_data(input_file, model_sampling_rate): |
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speech, sample_rate = librosa.load(input_file) |
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if len(speech.shape) > 1: |
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speech = speech[:, 0] + speech[:, 1] |
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if sample_rate != model_sampling_rate: |
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speech = librosa.resample(speech, sample_rate, model_sampling_rate) |
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return speech |
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feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-spanish") |
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sampling_rate = feature_extractor.sampling_rate |
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asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish") |
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model_name = 'hackathon-pln-es/t5-small-finetuned-spanish-to-quechua' |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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new_line = '\n' |
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def predict_and_ctc_lm_decode(input_file): |
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speech = load_and_fix_data(input_file, sampling_rate) |
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transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1) |
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transcribed_text = transcribed_text["text"] |
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input = tokenizer(transcribed_text, return_tensors="pt") |
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output = model.generate(input["input_ids"], max_length=40, num_beams=4, early_stopping=True) |
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output = tokenizer.decode(output[0], skip_special_tokens=True) |
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return f"Spanish Audio Transcription: {transcribed_text} {new_line} Quechua Translation :{output}" |
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gr.Interface( |
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predict_and_ctc_lm_decode, |
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inputs=[ |
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gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio") |
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], |
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outputs=[gr.outputs.Textbox()], |
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examples=[["audio_test.wav"], ["travel.wav"]], |
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title="Spanish-Audio-Transcriptions-to-Quechua-Translation", |
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description = "This is a Gradio demo of Spanish Audio Transcriptions to Quechua Translation. To use this, simply provide an audio input (audio recording or via microphone), which will subsequently be transcribed and translated to Quechua language.", |
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layout="horizontal", |
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theme="huggingface", |
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).launch(enable_queue=True, cache_examples=True) |
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