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import enum
from pydantic import BaseModel, Field
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"
# VTT = "vtt"
# SRT = "srt"
# https://huggingface.co/Systran
class Model(enum.StrEnum):
TINY_EN = "tiny.en"
TINY = "tiny"
BASE_EN = "base.en"
BASE = "base"
SMALL_EN = "small.en"
SMALL = "small"
MEDIUM_EN = "medium.en"
MEDIUM = "medium"
LARGE = "large"
LARGE_V1 = "large-v1"
LARGE_V2 = "large-v2"
LARGE_V3 = "large-v3"
DISTIL_SMALL_EN = "distil-small.en"
DISTIL_MEDIUM_EN = "distil-medium.en"
DISTIL_LARGE_V2 = "distil-large-v2"
DISTIL_LARGE_V3 = "distil-large-v3"
class Device(enum.StrEnum):
CPU = "cpu"
CUDA = "cuda"
AUTO = "auto"
# https://github.com/OpenNMT/CTranslate2/blob/master/docs/quantization.md
# NOTE: `Precision` might be a better name
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 WhisperConfig(BaseModel):
model: Model = Field(default=Model.MEDIUM_EN)
inference_device: Device = Field(default=Device.AUTO)
compute_type: Quantization = Field(default=Quantization.DEFAULT)
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` to `whisper.model`, etc.
"""
model_config = SettingsConfigDict(env_nested_delimiter="_")
log_level: str = "info"
default_language: Language | None = None
default_response_format: ResponseFormat = ResponseFormat.JSON
whisper: WhisperConfig = WhisperConfig()
max_models: int = 1
"""
Max duration to for the next audio chunk before transcription is finilized and connection is closed.
"""
max_no_data_seconds: float = 1.0
min_duration: float = 1.0
word_timestamp_error_margin: float = 0.2
"""
Max allowed audio duration without any speech being detected before transcription is finilized and connection is closed.
"""
max_inactivity_seconds: float = 5.0
"""
Controls how many latest seconds of audio are being passed through VAD.
Should be greater than `max_inactivity_seconds`
"""
inactivity_window_seconds: float = 10.0
config = Config()