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import gradio as gr
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import phonemizer
import librosa
import math
import io
import base64
from strsimpy.jaro_winkler import JaroWinkler
# base64 to audio ✅
# audio to transcription ✅
# audio to text ✅
# text to phoneme ✅
# accuracy = jarowinkler(transcription, phoneme) ✅
# band = getBandFromAccuracy(accuracy) ✅
# return accuracy, band ✅
def lark(audioAsB64):
# base64 to wav data conversion
wav_data = base64.b64decode(audioAsB64.encode("utf-8"))
# audio to transcription
processor = Wav2Vec2Processor.from_pretrained(
"facebook/wav2vec2-xlsr-53-espeak-cv-ft"
)
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
waveform, sample_rate = librosa.load(io.BytesIO(wav_data), sr=16000)
input_values = processor(
waveform, sampling_rate=sample_rate, return_tensors="pt"
).input_values
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
speechToPhonemeTranscription = processor.batch_decode(predicted_ids)[0]
# audio to text
processorSTT = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
input_values = processorSTT(
waveform, sampling_rate=sample_rate, return_tensors="pt"
).input_values
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
speechToTextTranscripition = processor.batch_decode(predicted_ids)[0]
# text to phoneme
graphemeToPhonemeTranscription = phonemizer.phonemize(speechToTextTranscripition)
# accuracy = jaroWinkler(transcription, phoneme)
jarowinkler = JaroWinkler()
similarity_score = jarowinkler.similarity(
speechToPhonemeTranscription, graphemeToPhonemeTranscription
)
# ielts pronunciation band estimation
def getBandFromSimilarityScore(similarity_score):
if similarity_score >= 0.91:
return 9
elif similarity_score >= 0.81:
return 8
elif similarity_score >= 0.73:
return 7
elif similarity_score >= 0.65:
return 6
elif similarity_score >= 0.60:
return 5
elif similarity_score >= 0.46:
return 4
elif similarity_score >= 0.35:
return 3
elif similarity_score >= 0.1:
return 2
else:
return 1
IELTSband = getBandFromSimilarityScore(similarity_score)
return [similarity_score, IELTSband]
iface = gr.Interface(fn=lark, inputs="text", outputs=["text", "text"])
iface.launch()
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