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isort & black

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- trying to fix environment issues
This commit is contained in:
German Mikheev
2025-09-14 03:07:06 +04:00
parent 7cf4c5259b
commit 7291b148e5
11 changed files with 177 additions and 99 deletions
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env_cpu.yml
+19
View File
@@ -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
+21
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@@ -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
+19
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@@ -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
-16
View File
@@ -1,16 +0,0 @@
name: notecast
channels:
- defaults
- conda-forge
dependencies:
- torchvision
- pytorch
- torchaudio
- transformers
- accelerate
- ffmpeg
- pyinstaller
- customtkinter
- isort
- mypy
- black
main.py
+1 -1
View File
@@ -2,4 +2,4 @@ from ui.ui import TranscriberApp
if __name__ == "__main__":
app = TranscriberApp()
app.mainloop()
app.mainloop()
transcription/audio_transcription.py
+60 -47
View File
@@ -1,43 +1,46 @@
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:
model_name = "openai/whisper-large-v2"
model_name = "openai/whisper-large-v2"
filepath: str
waveform: torch.Tensor
sampling_rate: int
chunks: list = []
batches: list = []
chunk_size: int
chunk_size: int
custom_chunk_length: int
custom_batch_length: int
device = "cuda"
processor: WhisperProcessor
model: WhisperForConditionalGeneration
logger: logging.Logger
torch_dtype: torch.dtype
language = "ru"
all_transcription: list = []
def __init__(
self,
self,
filepath: str,
device_configuration: DeviceConfiguration,
logger: logging.Logger,
language = "ru"
language="ru",
) -> None:
# TODO: add pretty docs here
self.filepath = filepath
@@ -50,44 +53,49 @@ class AudioTranscription:
self.custom_chunk_length = device_configuration.chunk_length_s
self.custom_batch_length = device_configuration.batch_size
self.torch_dtype = device_configuration.torch_dtype
self.chunks: list = []
self.batches: list = []
self.all_transcription: list = []
try:
logger.info("Loading model WhisperProcessor...")
self.processor = WhisperProcessor.from_pretrained(self.model_name)
self.model = WhisperForConditionalGeneration.from_pretrained(
self.model_name,
torch_dtype=self.torch_dtype
self.model_name, 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:
logger.error(f"Unable to load file {self.filepath}: {e}")
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:
self.waveform = self.waveform.mean(dim=0, keepdim=True)
self.waveform = self.waveform.squeeze(0)
def _split_to_chunks(self, chunk_length_s: int = 30) -> None:
self.logger.info(f"Splitting audio on chunks...")
self.chunk_size = chunk_length_s * 16000 # 16kHz after resampling
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)):
@@ -95,46 +103,51 @@ class AudioTranscription:
end = min((idx + 1) * self.chunk_size, total_samples)
chunk = self.waveform[start:end].cpu().numpy().astype("float32")
self.chunks.append(chunk)
def _resplit_to_batches(self) -> None:
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:
start_time = time.time()
try:
self.all_transcription = []
for idx in tqdm(range(len(self.batches))):
inputs = self.processor(
self.batches[idx],
self.batches[idx],
sampling_rate=16000,
return_tensors="pt",
padding=True
return_tensors="pt",
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()
@@ -142,4 +155,4 @@ class AudioTranscription:
self._split_to_chunks()
self._resplit_to_batches()
self._process_all_batches()
return " ".join(self.all_transcription)
return " ".join(self.all_transcription)
transcription/device_configuration.py
+12 -9
View File
@@ -1,6 +1,8 @@
from dataclasses import dataclass
import torch
@dataclass
class DeviceConfiguration:
"""
@@ -8,36 +10,37 @@ class DeviceConfiguration:
Attributes:
device (str): Type of device. Possible options: "cuda", "cpu", "mps".
model_name (str): Whisper models. Possible options:
- "openai/whisper-tiny"
- "openai/whisper-small"
- "openai/whisper-medium"
- "openai/whisper-large"
- "openai/whisper-large-v2"
batch_size (int): Chunks in one batch. Selected for VRAM.
chunk_length_s (int): Length of one audio chunk in seconds. Smaller -> less VRAM.
data_type (str): custom data type of model. Variants:
- torch.float16 - for GPUs
- 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
chunk_length_s: int = 30
data_type: str = "torch.float16"
_dtype_map = {
"torch.float16": torch.float16,
"torch.float32": torch.float32,
"torch.bfloat16": torch.bfloat16
"torch.bfloat16": torch.bfloat16,
}
torch_dtype: torch.dtype = None
def __post_init__(self):
self.torch_dtype = self._dtype_map[self.data_type]
self.torch_dtype = self._dtype_map[self.data_type]
transcription/torch_checker.py
+19 -13
View File
@@ -1,32 +1,38 @@
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")
logger.info("=== Check completed ===")
ui/configuration_actions.py
+11 -2
View File
@@ -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
+11 -9
View File
@@ -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)
@@ -242,4 +244,4 @@ class TranscriberApp(ctk.CTk):
messagebox.showerror("Error", str(e))
finally:
self.start_btn.configure(state="normal")
self.stop_btn.configure(state="disabled")
self.stop_btn.configure(state="disabled")
ui/ui_log_handler.py
+4 -2
View File
@@ -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")
@@ -21,4 +23,4 @@ def setup_ui_logger(text_widget: tk.Text, level=logging.INFO):
handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(message)s"))
logger.addHandler(handler)
return logger
return logger