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| import streamlit as st | |
| from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC | |
| import torch | |
| import numpy as np | |
| import soundfile as sf | |
| import io | |
| import librosa | |
| import matplotlib.pyplot as plt | |
| st.title("Syllables per Second Calculator") | |
| st.write("Upload an audio file to calculate the number of 'p', 't', and 'k' syllables per second.") | |
| def get_syllables_per_second(audio_file): | |
| processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft") | |
| model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft") | |
| audio_input, original_sample_rate = sf.read(io.BytesIO(audio_file.read())) | |
| target_sample_rate = processor.feature_extractor.sampling_rate | |
| # resample the sample rate if not 16 k | |
| if original_sample_rate != target_sample_rate: | |
| if audio_input.ndim > 1: | |
| audio_input = np.asarray([librosa.resample(channel, orig_sr=original_sample_rate, target_sr=target_sample_rate) for channel in audio_input.T]).T | |
| else: | |
| audio_input = librosa.resample(audio_input, orig_sr=original_sample_rate, target_sr=target_sample_rate) | |
| # make the audio mono if it is stereo | |
| if audio_input.ndim > 1 and audio_input.shape[1] == 2: | |
| audio_input = np.mean(audio_input, axis=1) | |
| input_values = processor(audio_input, return_tensors="pt").input_values | |
| with torch.no_grad(): | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.batch_decode(predicted_ids, output_char_offsets=True) | |
| offsets = transcription['char_offsets'] | |
| print("the offets are: ", offsets) | |
| # Find the start and end time offsets of the syllables | |
| syllable_offsets = [item for item in offsets[0] if item['char'] in ['p', 't', 'k']] | |
| if syllable_offsets: # if any syllable is found | |
| first_syllable_offset = syllable_offsets[0]['start_offset'] * 0.02 | |
| last_syllable_offset = syllable_offsets[-1]['end_offset'] * 0.02 | |
| print("the first syllable offset is: ", first_syllable_offset) | |
| print("the last syllable offset is: ", last_syllable_offset) | |
| # Duration from the first to the last syllable | |
| syllable_duration = last_syllable_offset - first_syllable_offset | |
| print("the syllable duration is: ", syllable_duration) | |
| else: | |
| syllable_duration = 0 | |
| syllable_count = len(syllable_offsets) | |
| audio_duration = len(audio_input) / target_sample_rate | |
| print("the audio duration is: ", audio_duration) | |
| print("the syllable count is: ", syllable_count) | |
| #print("the syllabels per second is: ", syllable_count / audio_duration) | |
| syllables_per_second = syllable_count / syllable_duration if syllable_duration > 0 else 0 | |
| times = [] | |
| syllables_per_second_time = [] | |
| for i in range(len(syllable_offsets) - 1): | |
| start = syllable_offsets[i]['start_offset'] * 0.02 | |
| end = syllable_offsets[i + 1]['end_offset'] * 0.02 | |
| duration = end - start | |
| rate = 1 / duration if duration > 0 else 0 | |
| times.append(start) | |
| syllables_per_second_time.append(rate) | |
| plt.plot(times, syllables_per_second_time) | |
| plt.xlabel('Time (s)') | |
| plt.ylabel('Syllables per second') | |
| # plt.show() | |
| # save the figure | |
| plt.savefig('syllables_per_second.png') | |
| # show the image using streamlit | |
| st.image('syllables_per_second.png') | |
| return syllables_per_second | |
| uploaded_file = st.file_uploader("Choose an audio file", type=["wav"]) | |
| if uploaded_file is not None: | |
| with st.spinner("Processing the audio file..."): | |
| result = get_syllables_per_second(uploaded_file) | |
| st.write("Syllables per second: ", result) |