74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
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import json
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from pathlib import Path
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import streamlit as st
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# import matplotlib.pyplot as plt
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# import numpy as np
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import librosa
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import librosa.display
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from pydub import AudioSegment
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from jasper.client import transcriber_pretrained, transcriber_speller
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# from pymongo import MongoClient
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st.title("ASR Speller Validation")
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dataset_path: Path = Path("/dataset/asr_data/call_alphanum_v3")
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manifest_path = dataset_path / Path("test_manifest.json")
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# print(manifest_path)
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with manifest_path.open("r") as pf:
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pnr_jsonl = pf.readlines()
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pnr_data = [json.loads(i) for i in pnr_jsonl]
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def main():
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# pnr_data = MongoClient("mongodb://localhost:27017/").test.asr_pnr
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# sample_no = 0
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sample_no = (
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st.slider(
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"Sample",
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min_value=1,
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max_value=len(pnr_data),
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value=1,
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step=1,
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format=None,
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key=None,
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)
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- 1
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)
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sample = pnr_data[sample_no]
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st.write(f"Sample No: {sample_no+1} of {len(pnr_data)}")
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audio_path = Path(sample["audio_filepath"])
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# st.write(f"Audio Path:{audio_path}")
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aud_seg = AudioSegment.from_wav(audio_path) # .set_channels(1).set_sample_width(2).set_frame_rate(24000)
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st.sidebar.text("Transcription")
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st.sidebar.text(f"Pretrained:{transcriber_pretrained(aud_seg.raw_data)}")
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st.sidebar.text(f"Speller:{transcriber_speller(aud_seg.raw_data)}")
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st.sidebar.text(f"Expected: {audio_path.stem}")
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spell_text = sample["text"]
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st.sidebar.text(f"Spelled: {spell_text}")
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st.audio(audio_path.open("rb"))
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selected = st.radio("The Audio is", ("Correct", "Incorrect", "Inaudible"))
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corrected = audio_path.stem
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if selected == "Incorrect":
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corrected = st.text_input("Actual:", value=corrected)
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# content = ''
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if sample_no > 0 and st.button("Previous"):
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sample_no -= 1
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if st.button("Next"):
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st.write(sample_no, selected, corrected)
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sample_no += 1
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(y, sr) = librosa.load(audio_path)
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librosa.display.waveplot(y=y, sr=sr)
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# arr = np.random.normal(1, 1, size=100)
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# plt.hist(arr, bins=20)
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st.sidebar.pyplot()
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# def main():
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# app()
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if __name__ == "__main__":
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main()
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