diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..0059473
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,28 @@
+# Используем образ ROCm с предустановленным PyTorch
+FROM rocm/pytorch:rocm6.4.1_ubuntu22.04_py3.10_pytorch_release_2.6.0
+
+# Устанавливаем рабочую директорию в контейнере
+WORKDIR /app
+
+# Устанавливаем системные зависимости
+RUN apt-get update && apt-get install -y \
+ ffmpeg \
+ python3-pip \
+ && rm -rf /var/lib/apt/lists/*
+
+# Устанавливаем зависимости Python
+COPY requirements.txt .
+RUN pip install --no-cache-dir --default-timeout=100 -r requirements.txt
+
+# Копируем остальные файлы приложения
+COPY . .
+
+# Открываем порт, на котором будет работать приложение
+EXPOSE 9854
+
+# Устанавливаем переменные окружения для ROCm
+ENV HSA_OVERRIDE_GFX_VERSION=10.3.0
+ENV PYTORCH_ROCM_ARCH=gfx1030
+
+# Команда для запуска приложения
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "9854", "--log-level", "debug"]
diff --git a/config.yaml b/config.yaml
deleted file mode 100644
index 406175b..0000000
--- a/config.yaml
+++ /dev/null
@@ -1,9 +0,0 @@
-server:
- host: "0.0.0.0"
- port: 8000
- ui: true
-
-whisper:
- model_name: "turbo"
- device: "cuda"
- compute_type: "int8"
\ No newline at end of file
diff --git a/converter.py b/converter.py
deleted file mode 100644
index 7d05819..0000000
--- a/converter.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import ffmpeg
-import os
-import tempfile
-import shutil
-
-def is_valid_format(file_path: str) -> bool:
- """Проверяет, является ли аудиофайл 16kHz моно WAV."""
- try:
- probe = ffmpeg.probe(file_path)
- audio_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'audio'), None)
- if audio_stream is None:
- return False
-
- return (
- audio_stream.get('codec_name') == 'pcm_s16le' and
- audio_stream.get('channels') == 1 and
- audio_stream.get('sample_rate') == '16000'
- )
- except ffmpeg.Error:
- return False
-
-def convert_to_wav(input_file_path: str) -> tuple[str, bool]:
- """
- Конвертирует аудиофайл в 16kHz моно WAV.
- Возвращает путь к сконвертированному файлу и флаг, указывающий, была ли выполнена конвертация.
- Если файл уже в нужном формате, возвращает исходный путь и False.
- """
- if is_valid_format(input_file_path):
- return input_file_path, False
-
- output_file_path = tempfile.mktemp(suffix=".wav")
-
- try:
- ffmpeg.input(input_file_path).output(
- output_file_path,
- acodec='pcm_s16le',
- ac=1,
- ar='16k'
- ).run(capture_stdout=True, capture_stderr=True)
- return output_file_path, True
- except ffmpeg.Error as e:
- if os.path.exists(output_file_path):
- os.remove(output_file_path)
- raise e
-
diff --git a/main.py b/main.py
deleted file mode 100644
index d6512c9..0000000
--- a/main.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import os
-import tempfile
-import sys
-import yaml
-from typing import Optional, List, Union, Tuple, Iterable
-from fastapi import FastAPI, UploadFile, File, Depends
-from pydantic import BaseModel
-from fastapi.responses import HTMLResponse
-from faster_whisper import WhisperModel
-from converter import convert_to_wav
-
-with open("config.yaml", 'r') as f:
- config = yaml.safe_load(f)
-
-app = FastAPI()
-
-w_config = config['whisper']
-
-class TranscriptionOptions(BaseModel):
- language: Optional[str] = w_config.get('language')
- task: str = w_config.get('task', 'transcribe')
- beam_size: int = w_config.get('beam_size', 5)
- best_of: int = w_config.get('best_of', 5)
- patience: float = w_config.get('patience', 1.0)
- length_penalty: float = w_config.get('length_penalty', 1.0)
- repetition_penalty: float = w_config.get('repetition_penalty', 1.0)
- no_repeat_ngram_size: int = w_config.get('no_repeat_ngram_size', 0)
- temperature: Union[float, List[float], Tuple[float, ...]] = w_config.get('temperature', [0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
- log_progress: bool = w_config.get('log_progress', False)
- compression_ratio_threshold: Optional[float] = w_config.get('compression_ratio_threshold', 2.4)
- log_prob_threshold: Optional[float] = w_config.get('log_prob_threshold', -1.0)
- no_speech_threshold: Optional[float] = w_config.get('no_speech_threshold', 0.6)
- condition_on_previous_text: bool = w_config.get('condition_on_previous_text', True)
- prompt_reset_on_temperature: float = w_config.get('prompt_reset_on_temperature', 0.5)
- initial_prompt: Optional[Union[str, Iterable[int]]] = w_config.get('initial_prompt')
- prefix: Optional[str] = w_config.get('prefix')
- suppress_blank: bool = w_config.get('suppress_blank', True)
- suppress_tokens: Optional[List[int]] = w_config.get('suppress_tokens', [-1])
- without_timestamps: bool = w_config.get('without_timestamps', False)
- max_initial_timestamp: float = w_config.get('max_initial_timestamp', 1.0)
- word_timestamps: bool = w_config.get('word_timestamps', False)
- prepend_punctuations: str = w_config.get('prepend_punctuations', '"\'“¿([{-')
- append_punctuations: str = w_config.get('append_punctuations', '"\'.。,，!！?？:：”)]}、')
- vad_filter: bool = w_config.get('vad_filter', False)
- vad_parameters: Optional[dict] = w_config.get('vad_parameters')
- max_new_tokens: Optional[int] = w_config.get('max_new_tokens')
- chunk_length: Optional[int] = w_config.get('chunk_length')
- clip_timestamps: Union[str, List[float]] = w_config.get('clip_timestamps', "0")
- hallucination_silence_threshold: Optional[float] = w_config.get('hallucination_silence_threshold')
- hotwords: Optional[str] = w_config.get('hotwords')
- language_detection_threshold: Optional[float] = w_config.get('language_detection_threshold')
- language_detection_segments: int = w_config.get('language_detection_segments', 1)
-
-class WhisperTranscriber:
- def __init__(self, model_name, device, compute_type):
- self.model = WhisperModel(model_name, device=device, compute_type=compute_type)
-
- def transcribe(self, audio_file_path: str, options: dict) -> str:
- segments, _ = self.model.transcribe(audio_file_path, **options)
- transcription = " ".join([segment.text for segment in segments])
- return transcription
-
-transcriber = WhisperTranscriber(
- model_name=w_config['model_name'],
- device=w_config['device'],
- compute_type=w_config['compute_type']
-)
-
-@app.post("/transcribe")
-async def transcribe_audio(file: UploadFile = File(...), options: TranscriptionOptions = Depends()):
- temp_audio_file_path = None
- converted_file_path = None
- was_converted = False
- try:
- with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as temp_audio_file:
- temp_audio_file.write(await file.read())
- temp_audio_file_path = temp_audio_file.name
-
- converted_file_path, was_converted = convert_to_wav(temp_audio_file_path)
-
- transcription = transcriber.transcribe(converted_file_path, options.dict(exclude_none=True))
-
- return {"transcription": transcription}
- finally:
- if temp_audio_file_path and os.path.exists(temp_audio_file_path):
- os.remove(temp_audio_file_path)
- if was_converted and converted_file_path and os.path.exists(converted_file_path):
- os.remove(converted_file_path)
-
-def create_ui():
- return '''
-
-
-
-
-
- Whisper Transcription
-
-
-
-
-
-
-
-
Upload Audio for Transcription
-
-
-
-
-
-
-
Transcription:
-
-
-
-