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import enum
from typing import Self
from pydantic import BaseModel, Field, model_validator
from pydantic_settings import BaseSettings, SettingsConfigDict
SAMPLES_PER_SECOND = 16000
BYTES_PER_SAMPLE = 2
BYTES_PER_SECOND = SAMPLES_PER_SECOND * BYTES_PER_SAMPLE
# 2 BYTES = 16 BITS = 1 SAMPLE
# 1 SECOND OF AUDIO = 32000 BYTES = 16000 SAMPLES
# https://platform.openai.com/docs/api-reference/audio/createTranscription#audio-createtranscription-response_format
class ResponseFormat(enum.StrEnum):
TEXT = "text"
JSON = "json"
VERBOSE_JSON = "verbose_json"
SRT = "srt"
VTT = "vtt"
class Device(enum.StrEnum):
CPU = "cpu"
CUDA = "cuda"
AUTO = "auto"
# https://github.com/OpenNMT/CTranslate2/blob/master/docs/quantization.md
class Quantization(enum.StrEnum):
INT8 = "int8"
INT8_FLOAT16 = "int8_float16"
INT8_BFLOAT16 = "int8_bfloat16"
INT8_FLOAT32 = "int8_float32"
INT16 = "int16"
FLOAT16 = "float16"
BFLOAT16 = "bfloat16"
FLOAT32 = "float32"
DEFAULT = "default"
class Language(enum.StrEnum):
AF = "af"
AM = "am"
AR = "ar"
AS = "as"
AZ = "az"
BA = "ba"
BE = "be"
BG = "bg"
BN = "bn"
BO = "bo"
BR = "br"
BS = "bs"
CA = "ca"
CS = "cs"
CY = "cy"
DA = "da"
DE = "de"
EL = "el"
EN = "en"
ES = "es"
ET = "et"
EU = "eu"
FA = "fa"
FI = "fi"
FO = "fo"
FR = "fr"
GL = "gl"
GU = "gu"
HA = "ha"
HAW = "haw"
HE = "he"
HI = "hi"
HR = "hr"
HT = "ht"
HU = "hu"
HY = "hy"
ID = "id"
IS = "is"
IT = "it"
JA = "ja"
JW = "jw"
KA = "ka"
KK = "kk"
KM = "km"
KN = "kn"
KO = "ko"
LA = "la"
LB = "lb"
LN = "ln"
LO = "lo"
LT = "lt"
LV = "lv"
MG = "mg"
MI = "mi"
MK = "mk"
ML = "ml"
MN = "mn"
MR = "mr"
MS = "ms"
MT = "mt"
MY = "my"
NE = "ne"
NL = "nl"
NN = "nn"
NO = "no"
OC = "oc"
PA = "pa"
PL = "pl"
PS = "ps"
PT = "pt"
RO = "ro"
RU = "ru"
SA = "sa"
SD = "sd"
SI = "si"
SK = "sk"
SL = "sl"
SN = "sn"
SO = "so"
SQ = "sq"
SR = "sr"
SU = "su"
SV = "sv"
SW = "sw"
TA = "ta"
TE = "te"
TG = "tg"
TH = "th"
TK = "tk"
TL = "tl"
TR = "tr"
TT = "tt"
UK = "uk"
UR = "ur"
UZ = "uz"
VI = "vi"
YI = "yi"
YO = "yo"
YUE = "yue"
ZH = "zh"
class Task(enum.StrEnum):
TRANSCRIBE = "transcribe"
TRANSLATE = "translate"
class WhisperConfig(BaseModel):
"""See https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/transcribe.py#L599."""
model: str = Field(default="Systran/faster-whisper-small")
"""
Default Huggingface model to use for transcription. Note, the model must support being ran using CTranslate2.
This model will be used if no model is specified in the request.
Models created by authors of `faster-whisper` can be found at https://huggingface.co/Systran
You can find other supported models at https://huggingface.co/models?p=2&sort=trending&search=ctranslate2 and https://huggingface.co/models?sort=trending&search=ct2
"""
inference_device: Device = Field(default=Device.AUTO)
device_index: int | list[int] = 0
compute_type: Quantization = Field(default=Quantization.DEFAULT)
cpu_threads: int = 0
num_workers: int = 1
class Config(BaseSettings):
"""Configuration for the application. Values can be set via environment variables.
Pydantic will automatically handle mapping uppercased environment variables to the corresponding fields.
To populate nested, the environment should be prefixed with the nested field name and an underscore. For example,
the environment variable `LOG_LEVEL` will be mapped to `log_level`, `WHISPER__MODEL`(note the double underscore) to `whisper.model`, to set quantization to int8, use `WHISPER__COMPUTE_TYPE=int8`, etc.
""" # noqa: E501
model_config = SettingsConfigDict(env_nested_delimiter="__")
log_level: str = "debug"
host: str = Field(alias="UVICORN_HOST", default="0.0.0.0")
port: int = Field(alias="UVICORN_PORT", default=8000)
allow_origins: list[str] | None = None
"""
https://docs.pydantic.dev/latest/concepts/pydantic_settings/#parsing-environment-variable-values
Usage:
`export ALLOW_ORIGINS='["http://localhost:3000", "http://localhost:3001"]'`
`export ALLOW_ORIGINS='["*"]'`
"""
enable_ui: bool = True
"""
Whether to enable the Gradio UI. You may want to disable this if you want to minimize the dependencies.
"""
default_language: Language | None = None
"""
Default language to use for transcription. If not set, the language will be detected automatically.
It is recommended to set this as it will improve the performance.
"""
default_response_format: ResponseFormat = ResponseFormat.JSON
whisper: WhisperConfig = WhisperConfig()
max_models: int = 1
"""
Maximum number of models that can be loaded at a time.
"""
preload_models: list[str] = Field(
default_factory=list,
examples=[
["Systran/faster-whisper-small"],
["Systran/faster-whisper-medium.en", "Systran/faster-whisper-small.en"],
],
)
"""
List of models to preload on startup. Shouldn't be greater than `max_models`. By default, the model is first loaded on first request.
""" # noqa: E501
max_no_data_seconds: float = 1.0
"""
Max duration to wait for the next audio chunk before transcription is finilized and connection is closed.
"""
min_duration: float = 1.0
"""
Minimum duration of an audio chunk that will be transcribed.
"""
word_timestamp_error_margin: float = 0.2
max_inactivity_seconds: float = 2.5
"""
Max allowed audio duration without any speech being detected before transcription is finilized and connection is closed.
""" # noqa: E501
inactivity_window_seconds: float = 5.0
"""
Controls how many latest seconds of audio are being passed through VAD.
Should be greater than `max_inactivity_seconds`
"""
@model_validator(mode="after")
def ensure_preloaded_models_is_lte_max_models(self) -> Self:
if len(self.preload_models) > self.max_models:
raise ValueError(
f"Number of preloaded models ({len(self.preload_models)}) is greater than max_models ({self.max_models})" # noqa: E501
)
return self
config = Config()