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import re
import requests
import pyarrow as pa
import librosa
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
from fastapi import FastAPI, File, UploadFile
import warnings
from starlette.formparsers import MultiPartParser
import io
import random
import tempfile
import os
MultiPartParser.max_file_size = 200 * 1024 * 1024
# Initialize FastAPI app
app = FastAPI()
# Load Wav2Vec2 tokenizer and model
tokenizer = Wav2Vec2Tokenizer.from_pretrained("./models/tokenizer")
model = Wav2Vec2ForCTC.from_pretrained("./models/model")
# Function to download English word list
def download_word_list():
print("Downloading English word list...")
url = "https://raw.githubusercontent.com/dwyl/english-words/master/words_alpha.txt"
response = requests.get(url)
words = set(response.text.split())
print("Word list downloaded.")
return words
english_words = download_word_list()
# Function to count correctly spelled words in text
def count_spelled_words(text, word_list):
print("Counting spelled words...")
# Split the text into words
words = re.findall(r'\b\w+\b', text.lower())
correct = sum(1 for word in words if word in word_list)
incorrect = len(words) - correct
print("Spelling check complete.")
return incorrect, correct
# Function to apply spell check to an item (assuming it's a dictionary)
def apply_spell_check(item, word_list):
print("Applying spell check...")
if isinstance(item, dict):
# This is a single item
text = item['transcription']
incorrect, correct = count_spelled_words(text, word_list)
item['incorrect_words'] = incorrect
item['correct_words'] = correct
print("Spell check applied to single item.")
return item
else:
# This is likely a batch
texts = item['transcription']
results = [count_spelled_words(text, word_list) for text in texts]
incorrect_counts, correct_counts = zip(*results)
item = item.append_column('incorrect_words', pa.array(incorrect_counts))
item = item.append_column('correct_words', pa.array(correct_counts))
print("Spell check applied to batch of items.")
return item
# FastAPI routes
@app.get('/')
async def root():
return "Welcome to the pronunciation scoring API!"
@app.post('/check_post')
async def rnc(number):
return {
"your value:" , number
}
@app.get('/check_get')
async def get_rnc():
return random.randint(0 , 10)
@app.post('/pronunciation_scoring')
async def upload_audio(file: UploadFile = File(...)):
# Create a temporary file to store the uploaded audio
with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as temp_file:
# Write the uploaded file content to the temporary file
content = await file.read()
temp_file.write(content)
temp_file_path = temp_file.name
# Load the audio file using librosa with a fixed sample rate of 16000 Hz
audio, sr = librosa.load(temp_file_path, sr=16000)
# Process the audio data as needed
# For this example, we'll just return some basic information
duration = librosa.get_duration(y=audio, sr=sr)
print("filename:" , file.filename)
print("duration:" , duration)
print("sample_rate:" , sr)
os.unlink(temp_file_path)
# Tokenize audio
print("Tokenizing audio...")
input_values = tokenizer(audio, return_tensors="pt").input_values
# Perform inference
print("Performing inference with Wav2Vec2 model...")
logits = model(input_values).logits
# Get predictions
print("Getting predictions...")
prediction = torch.argmax(logits, dim=-1)
# Decode predictions
print("Decoding predictions...")
transcription = tokenizer.batch_decode(prediction)[0]
# Convert transcription to lowercase
transcription = transcription.lower()
# Print transcription and word counts
print("Decoded transcription:", transcription)
incorrect, correct = count_spelled_words(transcription, english_words)
print("Spelling check - Incorrect words:", incorrect, ", Correct words:", correct)
# Calculate pronunciation score
fraction = correct / (incorrect + correct)
score = round(fraction * 100, 2)
print("Pronunciation score for", transcription, ":", score)
print("Pronunciation scoring process complete.")
return {
"transcription": transcription,
"pronunciation_score": score
} |