Update main.py

main.py

@@ -1,6 +1,97 @@
-import soundfile as sf
-import numpy as np
 
+
+
+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
+import numba
+import soundfile as sf 
+import asyncio
+
+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('/fluency_score')
 async def fluency_scoring(file: UploadFile = File(...)):
     with sf.SoundFile(file.file, 'r') as sound_file:
@@ -13,178 +104,81 @@ async def fluency_scoring(file: UploadFile = File(...)):
     
     print(audio_array)
     return audio_array[:5].tolist()
-
-
-# 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
-# import numba
-# import soundfile as sf 
-# import asyncio
-
-# 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('/fluency_score')
-# async def fluency_scoring(file: UploadFile = File(...)):
-#     audio_array, sample_rate = librosa.load(file.file, sr=16000)
-#     print(audio_array)
-#     return audio_array[:5]
     
 
-# @app.post('/pronunciation_score')
-# async def pronunciation_scoring(file: UploadFile = File(...)):    
-#     print("loading the file")
-#     url = "https://speech-processing-6.onrender.com/process_audio"
-#     files = {'file': await file.read()}
+@app.post('/pronunciation_score')
+async def pronunciation_scoring(file: UploadFile = File(...)):    
+    print("loading the file")
+    url = "https://speech-processing-6.onrender.com/process_audio"
+    files = {'file': await file.read()}
 
-#     print("file loaded")
+    print("file loaded")
         
-#     # print(files)
+    # print(files)
 
-#     print("making a POST request on speech processor")
+    print("making a POST request on speech processor")
     
-#     # Make the POST request
-#     response = requests.post(url, files=files)
+    # Make the POST request
+    response = requests.post(url, files=files)
 
-#     audio = response.json().get('audio_array')
+    audio = response.json().get('audio_array')
 
-#     print("audio:" , audio[:5])
+    print("audio:" , audio[:5])
     
     
 
-#     print("length of the audio array:" , len(audio))
+    print("length of the audio array:" , len(audio))
 
-#     print("*" * 100)
+    print("*" * 100)
 
-#     # Tokenization
-#     print("Tokenizing audio...")
-#     input_values = tokenizer(
-#         audio, 
-#         return_tensors="pt", 
-#         padding="max_length", 
-#         max_length= 386380, 
-#         truncation=True
-#     ).input_values
+    # Tokenization
+    print("Tokenizing audio...")
+    input_values = tokenizer(
+        audio, 
+        return_tensors="pt", 
+        padding="max_length", 
+        max_length= 386380, 
+        truncation=True
+    ).input_values
 
-#     print(input_values.shape)
+    print(input_values.shape)
     
-#     print("Tokenization complete. Shape of input_values:", input_values.shape)
+    print("Tokenization complete. Shape of input_values:", input_values.shape)
 
-#     return "tokenization successful" 
+    return "tokenization successful" 
     
-#     # Perform inference
-#     print("Performing inference with Wav2Vec2 model...")
+    # Perform inference
+    print("Performing inference with Wav2Vec2 model...")
     
-#     logits = model(input_values).logits
+    logits = model(input_values).logits
     
-#     print("Inference complete. Shape of logits:", logits.shape)
+    print("Inference complete. Shape of logits:", logits.shape)
 
-#     # Get predictions
-#     print("Getting predictions...")
-#     prediction = torch.argmax(logits, dim=-1)
-#     print("Prediction shape:", prediction.shape)
+    # Get predictions
+    print("Getting predictions...")
+    prediction = torch.argmax(logits, dim=-1)
+    print("Prediction shape:", prediction.shape)
 
-#     # Decode predictions
-#     print("Decoding predictions...")
-#     transcription = tokenizer.batch_decode(prediction)[0]
+    # Decode predictions
+    print("Decoding predictions...")
+    transcription = tokenizer.batch_decode(prediction)[0]
     
-#     # Convert transcription to lowercase
-#     transcription = transcription.lower()
+    # Convert transcription to lowercase
+    transcription = transcription.lower()
     
-#     print("Decoded transcription:", transcription)
+    print("Decoded transcription:", transcription)
     
-#     incorrect, correct = count_spelled_words(transcription, english_words)
-#     print("Spelling check - Incorrect words:", incorrect, ", Correct words:", correct)
+    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)
+    # Calculate pronunciation score
+    fraction = correct / (incorrect + correct)
+    score = round(fraction * 100, 2)
+    print("Pronunciation score for", transcription, ":", score)
     
-#     print("Pronunciation scoring process complete.")
+    print("Pronunciation scoring process complete.")
     
-#     return {
-#         "transcription": transcription,
-#         "pronunciation_score": score
-#     }
+    return {
+        "transcription": transcription,
+        "pronunciation_score": score
+    }