import random import Phonemize from Levenshtein import editops from gradio.components import Audio, Dropdown, Textbox, Image import gradio as gr import transcriber import json import pandas as pd import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram import numpy as np from torch import nn engine = transcriber.transcribe_SA(model_path='models/SA',verbose=0) phonemizer = Phonemize.phonemization() arpa2ipa = pd.read_csv('data/arpa2ipa.csv', sep='\\s+', header=None, names=['arpa','ipa']) prompts = np.loadtxt('data/prompts.txt', dtype=str) Attributes = engine.att_list df_output = None def select_prompt(): return random.choice(prompts) def phonemize_prompt(prompt, is_ipa=False): phonemes = phonemizer.cmu_phonemize(prompt) phonemes = [ph.lower() for ph in phonemes] if is_ipa: phonemes = [arpa2ipa[arpa2ipa.arpa==ph].ipa.values[0] for ph in phonemes] return ' '.join(phonemes) def diff_fn(): return [('H','+'),('E','-'),('N',None),('\n', None),('F','-'),('Fgo','-'),('M','+')] def recognizeAudio(audio_file, attributes, is_ipa=False): #print(','.join(attributes)) global df_output if is_ipa: p2att_matrix = 'data/p2att_en_us-ipa.csv' else: p2att_matrix = 'data/p2att_en_us-arpa.csv' output = engine.transcribe(audio_file, attributes= tuple(attributes), phonological_matrix_file=p2att_matrix, human_readable=False) records = [] d = json.loads(output) phonemes = d['Phoneme']['symbols'] #if is_ipa: # phonemes = [arpa2ipa[arpa2ipa.arpa==ph].ipa.values[0] for ph in phonemes] records.append(['Phoneme']+phonemes) for att in d['Attributes']: records.append([att['Name']]+att['Pattern']) df = pd.DataFrame.from_records(records) df.fillna('', inplace=True) df_output = df return df.to_html(header=False, index=False) #Get error by matching the expected sequence with the recognized one and return the output in a format that can be visualized by the gradio HighlightedText box def get_error(exp_list, rec_list): exp_list = list(exp_list) rec_list = list(rec_list) vocab = set(exp_list+rec_list) w2c = dict(zip(vocab,range(len(vocab)))) exp_out = [[a,None] for a in exp_list] rec_out = [[a,None] for a in rec_list] exp_enc = ''.join([chr(w2c[c]) for c in exp_list]) rec_enc = ''.join([chr(w2c[c]) for c in rec_list]) for op, exp_i, rec_i in editops(exp_enc, rec_enc): if op == 'replace': exp_out[exp_i][1] = 'S' rec_out[rec_i][1] = 'S' elif op == 'insert': rec_out[rec_i][1] = 'I' elif op == 'delete': exp_out[exp_i][1] = 'D' diff_list = [['Expected:\t', None]] + exp_out + [['\n',None]] + [['Recognized :\t', None]] + rec_out return diff_list def scale_vector(vector, new_min, new_max): min_val = min(vector) max_val = max(vector) scaled_vector = [] for val in vector: scaled_val = ((val - min_val) * (new_max - new_min) / (max_val - min_val)) + new_min scaled_vector.append(scaled_val) return scaled_vector def create_spectrogram_with_att(wav_file, att_contour, att ): # Read the WAV file sampling_rate, data = wavfile.read(wav_file) # Calculate the spectrogram f, t, Sxx = spectrogram(data, fs=sampling_rate) fig, axs = plt.subplots(2, 1, figsize=(10, 10), sharex=True) # Plot the spectrogram axs[0].pcolormesh(t, f, 10 * np.log10(Sxx), shading='gouraud') # Use grayscale colormap #plt.colorbar(label='Intensity (dB)') axs[0].set_ylabel('Frequency (Hz)') axs[0].set_xlabel('Time (s)') axs[0].set_title(f'Spectrogram with {att} Contour') axs[0].set_ylim(0, 8000) # Adjust the frequency range if necessary ax_att = axs[0].twinx() # Plot the att contour x_points = att_contour.shape[0] time_att = np.arange(0, x_points * 0.02, 0.02)[:x_points] # Assuming pitch_contour is sampled every 20 ms ax_att.plot(time_att, att_contour, color='blue', label=f'{att} Contour') ax_att.set_ylim(0,1) ax_att.legend() # Plot the waveform time = np.arange(0, len(data)) / sampling_rate axs[1].plot(time, data, color='blue') axs[1].set_ylabel('Amplitude') axs[1].set_xlabel('Time (s)') axs[1].set_title('Waveform') #plt.show() return fig def plot_contour(audio_file, att): indx_n = engine.processor.tokenizer.convert_tokens_to_ids([f'n_{att}'])[0] indx_p = engine.processor.tokenizer.convert_tokens_to_ids([f'p_{att}'])[0] index_all = [engine.processor.tokenizer.pad_token_id, indx_n, indx_p] prob = nn.functional.softmax(engine.logits.squeeze()[:,index_all], dim=-1) att_contour = prob[:,-1] fig = create_spectrogram_with_att(audio_file, att_contour, att) return fig with gr.Blocks() as gui: with gr.Tab("Main"): prompt = gr.Textbox(label='Prompt', value=select_prompt) get_prompt = gr.Button("Get Prompt") get_prompt.click(fn=select_prompt, outputs=prompt) with gr.Row(): with gr.Column(scale=3): prompt_phonemes = gr.Textbox(label="Expected Phonemes", interactive=False) with gr.Column(scale=1): is_ipa = gr.Checkbox(label="IPA") get_phoneme = gr.Button("Get Phonemes") get_phoneme.click(fn=phonemize_prompt, inputs=[prompt, is_ipa], outputs=prompt_phonemes) record_audio = gr.Audio(sources=["microphone","upload"], type="filepath") att_list = gr.Dropdown(label="Select Attributes", choices=sorted(Attributes), value=['vowel', 'voiced', 'consonant'] ,multiselect=True) process = gr.Button("Process Audio") recognition = gr.HTML(label='Output') process.click(fn=recognizeAudio, inputs=[record_audio,att_list, is_ipa], outputs=recognition) with gr.Tab("Assessment"): assess = gr.Button("Assessment") diff = [] for i in range(len(Attributes)+1): diff.append(gr.HighlightedText( combine_adjacent=False, show_legend=True, color_map={"S": "red", "I": "green", "D":"blue"}, visible=False)) def get_assessment(prompt_phonemes):#, recognized_phonemes, recognized_attributes): outputs = [gr.HighlightedText(visible=False)]*(len(Attributes)+1) outputs[0] = gr.HighlightedText(label=f"Phoneme Assessment", value=get_error(prompt_phonemes.split(), df_output.iloc[0].values[1:]), visible=True) i = 1 for i,r in df_output.iloc[1:].iterrows(): convert = lambda ph: '-' if f'n_{att}' in engine.p2att_map[ph] else '+' att = r.iloc[0] exp_att = [convert(ph) for ph in prompt_phonemes.split()] rec_att = r.iloc[1:].values outputs[i] = gr.HighlightedText(label=f"{att} Assessment", value=get_error(exp_att, rec_att), visible=True) i += 1 return outputs assess.click(fn=get_assessment, inputs= [prompt_phonemes], outputs=diff) with gr.Tab("Analysis"): selected_att = gr.Dropdown( sorted(Attributes), label="Select an Attribute to plot", value='voiced', interactive=True) do_plot = gr.Button('Plot') plot_block = gr.Plot(label='Spectrogram with Attribute Contour') do_plot.click(plot_contour, inputs=[record_audio,selected_att], outputs=plot_block) gui.launch()