| import gradio as gr | |
| import librosa | |
| from transformers import AutoFeatureExtractor, AutoModelForSeq2SeqLM, AutoTokenizer, pipeline | |
| 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 | |
| feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-spanish") | |
| sampling_rate = feature_extractor.sampling_rate | |
| asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish") | |
| model = AutoModelForSeq2SeqLM.from_pretrained('hackathon-pln-es/t5-small-spanish-nahuatl') | |
| tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/t5-small-spanish-nahuatl') | |
| def predict_and_ctc_lm_decode(input_file): | |
| speech = load_and_fix_data(input_file, sampling_rate) | |
| transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1) | |
| transcribed_text = transcribed_text["text"] | |
| input_ids = tokenizer('translate Spanish to Nahuatl: ' + transcribed_text, return_tensors='pt').input_ids | |
| outputs = model.generate(input_ids, max_length=512) | |
| outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] | |
| return f"Spanish Audio Transcription: {transcribed_text} & corresponding Nahuatl Translation is :{outputs}" | |
| gr.Interface( | |
| predict_and_ctc_lm_decode, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio") | |
| ], | |
| outputs=[gr.outputs.Textbox()], | |
| examples=[["audio1.wav"]], | |
| title="Spanish-Audio-Transcriptions-to-Nahuatl-Translation", | |
| article="<p><center><img src='........e'></center></p>", | |
| layout="horizontal", | |
| theme="huggingface", | |
| ).launch(enable_queue=True, cache_examples=True) | |