update

.dockerignore

@@ -1,3 +0,0 @@
-.venv
-__pycache__
-ReazonSpeech/

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,3 +0,0 @@
-.venv
-__pycache__
-ReazonSpeech/

.python-version

@@ -1 +0,0 @@
-3.11

Dockerfile

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-FROM nvidia/cuda:12.6.3-cudnn-devel-ubuntu20.04
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y tzdata \
-    curl \
-    git \
-    cmake \
-    build-essential \
-    ffmpeg \
-    && apt-get -y clean all
-
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-ENV PATH="/root/.local/bin/:$PATH"
-
-RUN uv python install 3.11
-
-WORKDIR /app
-RUN git clone https://github.com/reazon-research/ReazonSpeech
-
-COPY . .
-
-RUN uv sync
-
-CMD ["uv", "run", "python", "serve.py"]

README.md

@@ -1,10 +0,0 @@
----
-title: Stt Gpu Test
-emoji: 👁
-colorFrom: purple
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

docker-compose.yaml

@@ -1,9 +0,0 @@
-version: '3.8'
-
-services:
-  stt:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    ports:
-      - "8000:8000"

pyproject.toml

@@ -1,21 +0,0 @@
-[project]
-name = "stt"
-version = "0.1.0"
-description = "Add your description here"
-readme = "README.md"
-requires-python = ">=3.11"
-dependencies = [
-    "fastapi[all]>=0.115.12",
-    "numpy<2",
-    "pydub>=0.25.1",
-    "torch",
-    "reazonspeech-espnet-asr",
-]
-
-[[tool.uv.index]]
-name = "torch-cuda"
-url = "https://download.pytorch.org/whl/cu126"
-explicit = true
-
-[tool.uv.sources]
-reazonspeech-espnet-asr = { path = "ReazonSpeech/pkg/espnet-asr" }

serve.py

@@ -1,60 +0,0 @@
-from reazonspeech.espnet.asr import load_model, transcribe, audio_from_numpy
-from espnet2.bin.asr_inference import Speech2Text
-import torch
-from fastapi import FastAPI, HTTPException, UploadFile, File
-import uvicorn
-import numpy as np
-import io
-from pydub import AudioSegment
-import time
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-model = load_model(device)
-
-print(f"Model loaded on {device}")
-
-def transcribe_audio(audio_data_bytes):
-    try:
-        start_time = time.time()
-        audio_segment = AudioSegment.from_mp3(io.BytesIO(audio_data_bytes))
-        
-        # Get audio data as numpy array
-        audio_data_int16 = np.array(audio_segment.get_array_of_samples())
-        # Convert to float32 normalized to [-1, 1]
-        audio_data_float32 = audio_data_int16.astype(np.float32) / 32768.0
-        
-        # Process with reazonspeech
-        audio = audio_from_numpy(audio_data_float32, samplerate=audio_segment.frame_rate)
-        result = transcribe(model, audio)
-        end_time = time.time()
-        print(f"Time taken: {end_time - start_time} seconds")
-        return result
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-app = FastAPI()
-
-@app.post("/transcribe")
-async def transcribe_endpoint(file: UploadFile = File(...)):
-    audio_data = await file.read()
-    try:
-        result = transcribe_audio(audio_data)
-        return {
-            "result": [
-                {
-                    "text": result.text
-                }
-            ]
-        }
-    except HTTPException as e:
-        return {
-            "result": [
-                {
-                    "text": "エラーが発生しました, もう一度試してください",
-                }
-            ]
-        }
-    
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)