Update to use openai-whisper

system/tts_diff/tts_diff.py

@@ -2,46 +2,27 @@ import json
 import argparse
 from pathlib import Path
 import os
-import glob
-import platform
-import site
-import subprocess
 import sys
-# version 0.8
+import subprocess
-os.environ['KMP_DUPLICATE_LIB_OK']='True'
+# version 0.9
-
+import warnings
-def find_files_in_path_with_wildcard(pattern):
+# Filter Flash Attention warning
-    # Get the site-packages directory of the current Python environment
+warnings.filterwarnings("ignore", message="1Torch was not compiled with flash attention")
-    site_packages_path = site.getsitepackages()
+warnings.filterwarnings("ignore", message="Failed to launch Triton kernels, likely due to missing CUDA toolkit")
-    found_paths = []
+# Try to import whisper, install if not found
-    # Adjust the sub-directory based on the operating system
+try:
-    sub_directory = "nvidia/cublas"
+    import whisper
-    if platform.system() == "Linux":
+except ImportError:
-        sub_directory = os.path.join(sub_directory, "lib")
+    print("[AllTalk TTSDiff] OpenAI Whisper not found. Attempting to install...")
-    else:
+    try:
-        sub_directory = os.path.join(sub_directory, "bin")
+        subprocess.check_call([sys.executable, "-m", "pip", "install", "openai-whisper"])
-    
+        import whisper
-    # Iterate over each site-packages directory (there can be more than one)
+        print("[AllTalk TTSDiff] Successfully installed OpenAI Whisper!")
-    for directory in site_packages_path:
+    except Exception as e:
-        # Construct the search directory path
+        print("[AllTalk TTSDiff] Failed to install OpenAI Whisper:")
-        search_directory = os.path.join(directory, sub_directory)
+        print(f"[AllTalk TTSDiff] Error: {str(e)}")
-        # Use glob to find all files matching the pattern in this directory
+        print("[AllTalk TTSDiff] Please try manually installing with: pip install openai-whisper")
-        for file_path in glob.glob(os.path.join(search_directory, pattern)):
+        sys.exit(1)
-            if os.path.isfile(file_path):  # Ensure it's a file
-                found_paths.append(file_path)
-    return found_paths
-
-def detect_cublas():
-    # Use different file patterns based on the operating system
-    file_name_pattern = 'cublas64_11.*' if platform.system() == "Windows" else 'libcublas.so.11*'
-    found_paths = find_files_in_path_with_wildcard(file_name_pattern)
-    if found_paths:
-        print("[AllTalk TTSDiff] \033[94mCublas:\033[0m \033[92mDetected\033[0m", found_paths)
-    else:
-        print("[AllTalk TTSDiff] \033[94mCublas:\033[0m \033[91mERROR Not Detected\033[0m")
-        print("[AllTalk TTSDiff] \033[94mPlease install Cublas from the Nvidia CUDA Toolkit \033[0mhttps://developer.nvidia.com/cuda-downloads")
-        exit(1)
-        
 
 parser = argparse.ArgumentParser(description="Compare TTS output with the original text using detailed comparison.")
 parser.add_argument("--threshold", type=int, default=98, help="Similarity threshold for considering a match (default: 98)")
@@ -61,7 +42,7 @@ accuracy_threshold = args.threshold
 def disclaimer_text():
     print(f"\nDESCRIPTION: ")
     print(f"  This script is designed to assist in identifying bad TTS generation ID's from the TTS Generator.\n")
-    print(f"  This will download the Whisper Large-v2 model (if not already downloaded), and use it to compare your text to")
+    print(f"  This will download the Whisper Large-v3 model (if not already downloaded), and use it to compare your text to")
     print(f"  to the generated TTS. If it finds an issue, it will flag the ID number that has the issue. This does NOT 100%")
     print(f"  ensure that all TTS generated is perfect, sounds good etc, but it should flag up glaring issues and help with")
     print(f"  the process of identifying bad TTS generations.")
@@ -78,13 +59,10 @@ def disclaimer_text():
     print(f"  - The JSON file must be named \033[93mttsList.json\033[0m")
     print(f"  - When you have your ID list, go back into the TTS Generator, correct any lines and regenerate them. If you")
     print(f"    want to re-test everthing again after re-generating, you will need to export your list again and re-run this")
-    print(f"    script again, against your newly exported JSON list.")
+    print(f"    script again, against your newly exported JSON list.\n")
-    print(f"  - This requires access to \033[93mcublas64_11\033[0m, the same as Finetuning.")
-    print(f"    https://github.com/erew123/alltalk_tts/tree/main?#-important-requirements-cuda-118\n")
     return
 
 try:
-    from faster_whisper import WhisperModel
     import re
     import string
     import spacy
@@ -102,13 +80,10 @@ try:
     # Load a medium-sized spaCy model, adjust to "en_core_web_md" or "en_core_web_lg" as needed
     nlp = spacy.load("en_core_web_md")
 except Exception as e:
-    print("[AllTalk TTSDiff] Failed to load the spaCy language model.")
-    
-    # Attempt to download the spaCy model automatically
     try:
-        print("[AllTalk TTSDiff] Attempting to automatically download the spaCy language model...")
+        print("[AllTalk TTSDiff] SpaCy language model not installed.")            
+        print("[AllTalk TTSDiff] Attempting to automatically download the spaCy language model.")
         subprocess.run([sys.executable, "-m", "spacy", "download", "en_core_web_md"], check=True)    
-        
         # Try to load the model again after downloading
         nlp = spacy.load("en_core_web_md")
         print("[AllTalk TTSDiff] Model downloaded and loaded successfully.")
@@ -141,7 +116,7 @@ def normalize_text(text):
     text = text.lower()
     
     # Standardize and then remove quotation marks
-    text = text.replace("“", '"').replace("”", '"').replace("‘", "'").replace("’", "'")
+    text = text.replace(""", '"').replace(""", '"').replace("'", "'").replace("'", "'")
     text = text.translate(str.maketrans('', '', '"\''))
     
     # Remove all other punctuation except hyphens to preserve compound words
@@ -188,15 +163,12 @@ def transcribe_and_compare(file_url, original_text, model, item_id, flagged_ids)
         return  # Skip this file if it can't be accessed
 
     try:
-        segments, info = model.transcribe(str(audio_file_path), beam_size=5)
+        result = model.transcribe(str(audio_file_path))
-        transcribed_text = " ".join([segment.text for segment in segments])
+        transcribed_text = result["text"]
     except Exception as e:
         print(f"[AllTalk TTSDiff] Error transcribing file {audio_file_path}: {e}")
         return  # Skip this file if transcription fails
     
-    segments, info = model.transcribe(str(audio_file_path), beam_size=5)
-    transcribed_text = " ".join([segment.text for segment in segments])
-    
     # Normalize texts for comparison
     original_text_normalized = normalize_text(original_text)
     transcribed_text_normalized = normalize_text(transcribed_text)
@@ -210,7 +182,6 @@ def transcribe_and_compare(file_url, original_text, model, item_id, flagged_ids)
     
     # Only log and flag IDs for review if there's a mismatch or detected issues
     if not is_match:
-        #print(f"\033[93mMismatch Detected - ID:\033[0m {item_id}")
         print(f"[AllTalk TTSDiff] \033[93mText:\033[0m {original_text}")
         print(f"[AllTalk TTSDiff] \033[91mTTS :\033[0m{transcribed_text}")
         if has_spoken_punctuation:
@@ -225,10 +196,11 @@ def main():
     print("[AllTalk TTSDiff] \033[94mJSON file  :\033[92m", json_file_path, "\033[0m")
     print("[AllTalk TTSDiff] \033[94mWAV files  :\033[92m", wav_file_path, "\033[0m")
     print("[AllTalk TTSDiff] \033[94mAccuracy   :\033[92m", accuracy_threshold, "%", "\033[0m")
-    detect_cublas()
+    
-    model_size = "large-v2"
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    model = WhisperModel(model_size, device=device, compute_type="float32")
+    print("[AllTalk TTSDiff] \033[94mDevice     :\033[92m", device, "\033[0m")
+    print("[AllTalk TTSDiff] Loading Whisper Large-v3 model...")
+    model = whisper.load_model("large-v3", device=device)
     
     flagged_ids = []  # Initialize the list to track IDs needing review