isort & black

- trying to fix environment issues

env_cpu.yml

@@ -0,0 +1,19 @@
+name: notecast
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.10
+  - pytorch::pytorch=2.0.1
+  - pytorch::torchvision=0.15.2
+  - pytorch::torchaudio=2.0.2
+  - transformers=4.30.0
+  - accelerate=0.20.0
+  - ffmpeg
+  - pyinstaller
+  - customtkinter
+  - isort
+  - mypy
+  - black
+  - tqdm

env_cuda.yml

@@ -0,0 +1,21 @@
+name: notecast
+channels:
+  - pytorch
+  - nvidia
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.10
+  - pytorch::pytorch=2.0.1
+  - pytorch::torchvision=0.15.2
+  - pytorch::torchaudio=2.0.2
+  - pytorch::cudatoolkit=11.8
+  - transformers=4.30.0
+  - accelerate=0.20.0
+  - ffmpeg
+  - pyinstaller
+  - customtkinter
+  - isort
+  - mypy
+  - black
+  - tqdm

env_mps.yml

@@ -0,0 +1,19 @@
+name: notecast
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.10
+  - pytorch::pytorch=2.0.1
+  - pytorch::torchvision=0.15.2
+  - pytorch::torchaudio=2.0.2
+  - transformers=4.30.0
+  - accelerate=0.20.0
+  - ffmpeg
+  - pyinstaller
+  - customtkinter
+  - isort
+  - mypy
+  - black
+  - tqdm

environment.yml

@@ -1,16 +0,0 @@
-name: notecast
-channels:
-  - defaults
-  - conda-forge
-dependencies:
-  - torchvision
-  - pytorch
-  - torchaudio
-  - transformers
-  - accelerate
-  - ffmpeg
-  - pyinstaller
-  - customtkinter
-  - isort
-  - mypy
-  - black

transcription/audio_transcription.py

@@ -1,12 +1,15 @@
-from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import logging
+import math
+import time
+
 import torch
 import torchaudio
-from ui.ui_log_handler import UILogHandler
-from transcription.device_configuration import DeviceConfiguration
-import logging
-import time
-import math
 from tqdm import tqdm
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+from transcription.device_configuration import DeviceConfiguration
+from ui.ui_log_handler import UILogHandler
+
 
 # TODO: implement transcription with shift
 class AudioTranscription:
@@ -37,7 +40,7 @@ class AudioTranscription:
         filepath: str,
         device_configuration: DeviceConfiguration,
         logger: logging.Logger,
-        language = "ru"
+        language="ru",
     ) -> None:
         # TODO: add pretty docs here
         self.filepath = filepath
@@ -59,13 +62,14 @@ class AudioTranscription:
             self.processor = WhisperProcessor.from_pretrained(self.model_name)
 
             self.model = WhisperForConditionalGeneration.from_pretrained(
-                self.model_name,
+                self.model_name, torch_dtype=self.torch_dtype
-                torch_dtype=self.torch_dtype
             ).to(self.device)
 
             logger.info("Model loaded.")
 
-            self.waveform, self.sampling_rate = torchaudio.load(filepath, format="mp3", backend="ffmpeg")
+            self.waveform, self.sampling_rate = torchaudio.load(
+                filepath, format="mp3", backend="ffmpeg"
+            )
             logger.info(f"Successfully loaded file {filepath}.")
 
         except Exception as e:
@@ -73,7 +77,9 @@ class AudioTranscription:
             raise
 
     def _resample(self) -> None:
-        self.waveform = torchaudio.functional.resample(self.waveform, self.sampling_rate, 16000)
+        self.waveform = torchaudio.functional.resample(
+            self.waveform, self.sampling_rate, 16000
+        )
 
     def _to_mono(self):
         if self.waveform.shape[0] > 1:
@@ -87,7 +93,9 @@ class AudioTranscription:
         total_samples = self.waveform.shape[0]
         chunks_count = (total_samples + self.chunk_size - 1) // self.chunk_size
 
-        self.logger.info(f"File length - {total_samples / 16000:.1f} seconds, splitting on {chunks_count} chunks by {chunk_length_s} seconds per chunk.")
+        self.logger.info(
+            f"File length - {total_samples / 16000:.1f} seconds, splitting on {chunks_count} chunks by {chunk_length_s} seconds per chunk."
+        )
 
         self.chunks = []
         for idx in tqdm(range(chunks_count)):
@@ -100,7 +108,7 @@ class AudioTranscription:
         self.logger.info(f"Splitting chunks into batches...")
         self.batches = []
         for i in range(0, len(self.chunks), self.custom_batch_length):
-            batch = self.chunks[i:i + self.custom_batch_length]
+            batch = self.chunks[i : i + self.custom_batch_length]
             self.batches.append(batch)
 
     def _process_all_batches(self) -> None:
@@ -113,28 +121,33 @@ class AudioTranscription:
                     self.batches[idx],
                     sampling_rate=16000,
                     return_tensors="pt",
-                    padding=True
+                    padding=True,
                 )
 
-                input_features = inputs.input_features.to(self.device).to(self.torch_dtype)
+                input_features = inputs.input_features.to(self.device).to(
+                    self.torch_dtype
+                )
 
                 with torch.no_grad():
                     predicted_ids = self.model.generate(
                         input_features,
                         language=self.language,
                         task="transcribe",
-                        temperature=0.0
+                        temperature=0.0,
                     )
 
-                texts = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)
+                texts = self.processor.batch_decode(
+                    predicted_ids, skip_special_tokens=True
+                )
                 self.all_transcription.extend(texts)
             end_time = time.time()
-            self.logger.info(f"Transcription completed in {end_time - start_time:.2f} seconds")
+            self.logger.info(
+                f"Transcription completed in {end_time - start_time:.2f} seconds"
+            )
 
         except Exception as e:
             self.logger.error(f"Errors occured while processing chunks: {e}")
 
-    
     def transcribe_audio(self) -> str:
         # TODO: maybe something else, not str?
         self._resample()

transcription/device_configuration.py

@@ -1,6 +1,8 @@
 from dataclasses import dataclass
+
 import torch
 
+
 @dataclass
 class DeviceConfiguration:
     """
@@ -25,6 +27,7 @@ class DeviceConfiguration:
             - torch.float32 - for CPU / weak GPU
             - torch.bfloat16 - for GPUs which has BF16 support
     """
+
     device: str = "cuda"
     model_name: str = "openai/whisper-large-v2"
     batch_size: int = 16
@@ -34,7 +37,7 @@ class DeviceConfiguration:
     _dtype_map = {
         "torch.float16": torch.float16,
         "torch.float32": torch.float32,
-        "torch.bfloat16": torch.bfloat16
+        "torch.bfloat16": torch.bfloat16,
     }
 
     torch_dtype: torch.dtype = None

transcription/torch_checker.py

@@ -1,31 +1,37 @@
-import torch
-from dataclasses import dataclass
 import logging
+from dataclasses import dataclass
+
+import torch
+
 
 def check_torch(logger: logging.Logger) -> None:
     logger.info("=== Checking PyTorch ===")
     logger.info(f"Torch version: {torch.__version__}")
 
-    # === NVIDIA / AMD (CUDA API) ===
+    # NVIDIA / AMD (CUDA API)
     if torch.cuda.is_available():
         backend = "CUDA"
         if torch.version.hip is not None:
             backend = "ROCm (AMD HIP)"
 
         logger.info(f"{backend} backend is available")
-        logger.info(f"Compiled with: CUDA {torch.version.cuda}, ROCm {torch.version.hip}")
+        logger.info(
+            f"Compiled with: CUDA {torch.version.cuda}, ROCm {torch.version.hip}"
+        )
         logger.info(f"Number of devices: {torch.cuda.device_count()}")
 
         for i in range(torch.cuda.device_count()):
             logger.info(f"GPU {i}: {torch.cuda.get_device_name(i)}")
 
-    # ===   Apple Silicon (MPS)   ===
+    # Apple Silicon (MPS)
     elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
         logger.info("MPS backend is available (Apple Silicon)")
-        logger.info(f"MPS version: {getattr(torch.backends.mps, '__version__', 'unknown')}")
+        logger.info(
+            f"MPS version: {getattr(torch.backends.mps, '__version__', 'unknown')}"
+        )
         logger.info("GPU: Apple Silicon (Metal)")
 
-    # ===       CPU only mode     ===
+    # CPU only mode
     else:
         logger.info("Only CPU is available")
 

ui/configuration_actions.py

@@ -1,9 +1,18 @@
 from transcription.device_configuration import DeviceConfiguration
 
+
 # TODO: implement saving & removing configuration
 def save_configuration(cfg: DeviceConfiguration):
     config = {
         "Model": cfg.model_name,
         "Batch Size": cfg.batch_size,
-        "Data Type": cfg.data_type
+        "Data Type": cfg.data_type,
     }
+
+
+def load_config():
+    pass
+
+
+def delete_config():
+    pass

ui/ui.py

@@ -1,17 +1,16 @@
 import os
-import threading
 import queue
+import threading
 import tkinter as tk
-from tkinter import scrolledtext, filedialog, messagebox
+from tkinter import filedialog, messagebox, scrolledtext
 
 import customtkinter as ctk
 import torch
 
-from ui.ui_log_handler import setup_ui_logger
-from transcription.torch_checker import check_torch
-from transcription.device_configuration import DeviceConfiguration
 from transcription.audio_transcription import AudioTranscription
-
+from transcription.device_configuration import DeviceConfiguration
+from transcription.torch_checker import check_torch
+from ui.ui_log_handler import setup_ui_logger
 
 WINDOW_WIDTH = 900
 WINDOW_HEIGHT = 650
@@ -175,7 +174,10 @@ class TranscriberApp(ctk.CTk):
     def _browse_input(self):
         path = filedialog.askopenfilename(
             title="Select input audio file",
-            filetypes=[("Audio files", "*.wav *.mp3 *.m4a *.flac"), ("All files", "*.*")]
+            filetypes=[
+                ("Audio files", "*.wav *.mp3 *.m4a *.flac"),
+                ("All files", "*.*"),
+            ],
         )
         if path:
             self.input_file_var.set(path)
@@ -184,7 +186,7 @@ class TranscriberApp(ctk.CTk):
         path = filedialog.asksaveasfilename(
             title="Select output file",
             defaultextension=".txt",
-            filetypes=[("Text files", "*.txt"), ("All files", "*.*")]
+            filetypes=[("Text files", "*.txt"), ("All files", "*.*")],
         )
         if path:
             self.output_file_var.set(path)

ui/ui_log_handler.py

@@ -1,5 +1,6 @@
-import tkinter as tk
 import logging
+import tkinter as tk
+
 
 class UILogHandler(logging.Handler):
     def __init__(self, text_widget: tk.Text):
@@ -11,6 +12,7 @@ class UILogHandler(logging.Handler):
         self.text_widget.insert(tk.END, log_entry + "\n")
         self.text_widget.see(tk.END)
 
+
 # TODO: maybe some tqdm here, not in console?
 def setup_ui_logger(text_widget: tk.Text, level=logging.INFO):
     logger = logging.getLogger("UI_LOGGER")