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import os
import spacy
import speech_recognition as sr
from loguru import logger
from pydub import AudioSegment
# Load spaCy's English language model for grammar and text analysis
def get_doc(text):
try:
nlp = spacy.load("en_core_web_sm")
doc = nlp(text)
return doc
except OSError as ex:
logger.exception("Error while getting Spacy doc: ", str(ex))
logger.info("Please download the model with this command: python -m spacy download en_core_web_sm")
# Convert MP3 to WAV using pydub
def convert_mp3_to_wav(mp3_file, wav_file="converted_comprehension_audio.wav"):
sound = AudioSegment.from_mp3(mp3_file)
sound.export(wav_file, format="wav")
return wav_file
# Convert WAV to text using SpeechRecognition
def transcribe_audio(wav_file):
recognizer = sr.Recognizer()
with sr.AudioFile(wav_file) as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_google(audio_data)
return text
def split_audio(audio_path, chunk_length_ms=30000):
"""Splits audio into chunks of specified length (in milliseconds)."""
audio = AudioSegment.from_wav(audio_path)
chunks = [audio[i : i + chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
chunk_paths = []
for idx, chunk in enumerate(chunks):
chunk_path = f"{audio_path}_chunk_{idx}.wav"
chunk.export(chunk_path, format="wav")
chunk_paths.append(chunk_path)
return chunk_paths
def aggregate_scores(chunk_scores):
"""Aggregate scores across all chunks to produce a final score."""
final_score = {
"accuracy_score": 0,
"fluency_score": 0,
"completeness_score": 0,
"pronunciation_score": 0,
"mispronunced_words": [],
"display_text": "",
}
num_chunks = len(chunk_scores)
# Sum each score across chunks
for score in chunk_scores:
final_score["accuracy_score"] += score.get("accuracy_score", 0)
final_score["fluency_score"] += score.get("fluency_score", 0)
final_score["completeness_score"] += score.get("completeness_score", 0)
final_score["pronunciation_score"] += score.get("pronunciation_score", 0)
final_score["mispronunced_words"].extend(score.get("mispronunced_words"))
final_score["display_text"] += score.get("display_text")
# Average each score
for key in final_score:
if type(final_score[key]) in [float, int]:
final_score[key] = round(final_score[key] / num_chunks, 2)
return final_score
def remove_files(file_list):
try:
os.system("rm " + " ".join(file_list))
logger.info("All listed files removed.")
except Exception as ex:
logger.exception(f"Error removing files: {ex}")
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