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from phonemizer.separator import Separator
from phonemizer import phonemize
# from phonemizer.backend.espeak.wrapper import EspeakWrapper
from Levenshtein import distance as levenshtein_distance
from scoring import calculate_fluency_and_pronunciation
import whisper
import torch
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
model = whisper.load_model("base.en", device=device)
separator = Separator(phone=None, word='',)
# EspeakWrapper.set_library(r"C:\Program Files\eSpeak NG\libespeak-ng.dll")
def transcribe(audio):
result = model.transcribe(audio, word_timestamps=False, no_speech_threshold=0.4, compression_ratio_threshold=2, temperature=0)
return {'language': result['language'], 'text': result['text']}
def text2phoneme(text):
return phonemize(text.lower().split(), backend='espeak' , separator=separator, strip=True, with_stress=False, tie=False, language='en-us')
def rate_pronunciation(expected_phonemes, actual_phonemes):
expected_phonemes = expected_phonemes
actual_phonemes = actual_phonemes
# Calculate the Levenshtein distance between the two phoneme sequences
results = []
for i, base_word in enumerate(actual_phonemes):
best_dist = float('inf')
if i <= len(expected_phonemes):
for j in range(max(0, i-1), i + min(3, len(expected_phonemes) - i)):
dist = levenshtein_distance(expected_phonemes[j], base_word,)
if dist < best_dist:
best_dist = dist
if best_dist == 0: # Early stopping on perfect match
break
error_threshold = len(base_word) * 0.40
if best_dist == 0:
results.append(3)
elif best_dist <= error_threshold:
results.append(2)
else:
results.append(1)
return results
def Speaker_speech_analysis(audio_path, text):
pre_transcribtion = transcribe(audio_path)['text']
print(pre_transcribtion)
transcribtion = text2phoneme(pre_transcribtion)
text_phone = text2phoneme(text)
scores = rate_pronunciation(transcribtion, text_phone)
FP_scores = calculate_fluency_and_pronunciation(audio_path, len(pre_transcribtion.split()), scores, len(text.split()))
word_scores = [(word, s) for word, s in zip(text.split(), scores)]
FP_scores['word_scores'] = word_scores
return FP_scores
if __name__ == '__main__':
text = 'i have ADHD '
text = text2phoneme(text)
file_path = r'user_recording.wav'
trans = transcribe(file_path)['text']
print(trans)
trans = text2phoneme(trans)
print('base:', text)
print('predicted:', trans)
result = rate_pronunciation(trans, text)
print(result)
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