File size: 3,389 Bytes
a2bb355 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
from pydantic import BaseModel
from environs import Env
from typing import List, Dict, Any
import os
import base64
import numpy as np
import librosa
from scipy.io import wavfile
import asyncio
class EndpointHandler:
def __init__(self, model_dir=None):
self.model_dir = model_dir
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
try:
# Clone the repository
repo_url = "https://huggingface.co/mazalaai/TTS_Mongolian.git"
os.system(f"git clone {repo_url}")
# Change directory to the cloned repository
repo_dir = "TTS_Mongolian"
os.chdir(repo_dir)
# Import the voice_processing module and functions
from voice_processing import tts, get_model_names, voice_mapping, get_unique_filename
if "inputs" in data: # Check if data is in Hugging Face JSON format
return self.process_hf_input(data)
else:
return self.process_json_input(data)
except ValueError as e:
return {"error": str(e)}
except Exception as e:
return {"error": str(e)}
def process_json_input(self, json_data):
if all(key in json_data for key in ["model_name", "tts_text", "selected_voice", "slang_rate", "use_uploaded_voice"]):
model_name = json_data["model_name"]
tts_text = json_data["tts_text"]
selected_voice = json_data["selected_voice"]
slang_rate = json_data["slang_rate"]
use_uploaded_voice = json_data["use_uploaded_voice"]
voice_upload_file = json_data.get("voice_upload_file", None)
edge_tts_voice = voice_mapping.get(selected_voice)
if not edge_tts_voice:
raise ValueError(f"Invalid voice '{selected_voice}'.")
info, edge_tts_output_path, tts_output_data, edge_output_file = asyncio.run(tts(
model_name, tts_text, edge_tts_voice, slang_rate, use_uploaded_voice, voice_upload_file
))
if edge_output_file and os.path.exists(edge_output_file):
os.remove(edge_output_file)
_, audio_output = tts_output_data
audio_file_path = self.save_audio_data_to_file(audio_output) if isinstance(audio_output, np.ndarray) else audio_output
try:
with open(audio_file_path, 'rb') as file:
audio_bytes = file.read()
audio_data_uri = f"data:audio/wav;base64,{base64.b64encode(audio_bytes).decode('utf-8')}"
except Exception as e:
raise Exception(f"Failed to read audio file: {e}")
finally:
if os.path.exists(audio_file_path):
os.remove(audio_file_path)
return {"info": info, "audio_data_uri": audio_data_uri}
else:
raise ValueError("Invalid JSON structure.")
def process_hf_input(self, hf_data):
if "inputs" in hf_data:
actual_data = hf_data["inputs"]
return self.process_json_input(actual_data)
else:
return {"error": "Invalid Hugging Face JSON structure."}
def save_audio_data_to_file(self, audio_data, sample_rate=40000):
file_path = get_unique_filename('wav')
wavfile.write(file_path, sample_rate, audio_data)
return file_path |