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import sys
import logging
import io
import soundfile as sf
import math
try:
import torch
except ImportError:
torch = None
from typing import List
import numpy as np
from timed_objects import ASRToken
logger = logging.getLogger(__name__)
class ASRBase:
sep = " " # join transcribe words with this character (" " for whisper_timestamped,
# "" for faster-whisper because it emits the spaces when needed)
def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr):
self.logfile = logfile
self.transcribe_kargs = {}
if lan == "auto":
self.original_language = None
else:
self.original_language = lan
self.model = self.load_model(modelsize, cache_dir, model_dir)
def with_offset(self, offset: float) -> ASRToken:
# This method is kept for compatibility (typically you will use ASRToken.with_offset)
return ASRToken(self.start + offset, self.end + offset, self.text)
def __repr__(self):
return f"ASRToken(start={self.start:.2f}, end={self.end:.2f}, text={self.text!r})"
def load_model(self, modelsize, cache_dir, model_dir):
raise NotImplementedError("must be implemented in the child class")
def transcribe(self, audio, init_prompt=""):
raise NotImplementedError("must be implemented in the child class")
def use_vad(self):
raise NotImplementedError("must be implemented in the child class")
class WhisperTimestampedASR(ASRBase):
"""Uses whisper_timestamped as the backend."""
sep = " "
def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
import whisper
import whisper_timestamped
from whisper_timestamped import transcribe_timestamped
self.transcribe_timestamped = transcribe_timestamped
if model_dir is not None:
logger.debug("ignoring model_dir, not implemented")
return whisper.load_model(modelsize, download_root=cache_dir)
def transcribe(self, audio, init_prompt=""):
result = self.transcribe_timestamped(
self.model,
audio,
language=self.original_language,
initial_prompt=init_prompt,
verbose=None,
condition_on_previous_text=True,
**self.transcribe_kargs,
)
return result
def ts_words(self, r) -> List[ASRToken]:
"""
Converts the whisper_timestamped result to a list of ASRToken objects.
"""
tokens = []
for segment in r["segments"]:
for word in segment["words"]:
token = ASRToken(word["start"], word["end"], word["text"])
tokens.append(token)
return tokens
def segments_end_ts(self, res) -> List[float]:
return [segment["end"] for segment in res["segments"]]
def use_vad(self):
self.transcribe_kargs["vad"] = True
def set_translate_task(self):
self.transcribe_kargs["task"] = "translate"
class FasterWhisperASR(ASRBase):
"""Uses faster-whisper as the backend."""
sep = ""
def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
from faster_whisper import WhisperModel
if model_dir is not None:
logger.debug(f"Loading whisper model from model_dir {model_dir}. "
f"modelsize and cache_dir parameters are not used.")
model_size_or_path = model_dir
elif modelsize is not None:
model_size_or_path = modelsize
else:
raise ValueError("Either modelsize or model_dir must be set")
device = "cuda" if torch and torch.cuda.is_available() else "cpu"
compute_type = "float16" if device == "cuda" else "float32"
model = WhisperModel(
model_size_or_path,
device=device,
compute_type=compute_type,
download_root=cache_dir,
)
return model
def transcribe(self, audio: np.ndarray, init_prompt: str = "") -> list:
segments, info = self.model.transcribe(
audio,
language=self.original_language,
initial_prompt=init_prompt,
beam_size=5,
word_timestamps=True,
condition_on_previous_text=True,
**self.transcribe_kargs,
)
return list(segments)
def ts_words(self, segments) -> List[ASRToken]:
tokens = []
for segment in segments:
if segment.no_speech_prob > 0.9:
continue
for word in segment.words:
token = ASRToken(word.start, word.end, word.word, probability=word.probability)
tokens.append(token)
return tokens
def segments_end_ts(self, segments) -> List[float]:
return [segment.end for segment in segments]
def use_vad(self):
self.transcribe_kargs["vad_filter"] = True
def set_translate_task(self):
self.transcribe_kargs["task"] = "translate"
class MLXWhisper(ASRBase):
"""
Uses MLX Whisper optimized for Apple Silicon.
"""
sep = ""
def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
from mlx_whisper.transcribe import ModelHolder, transcribe
import mlx.core as mx
if model_dir is not None:
logger.debug(f"Loading whisper model from model_dir {model_dir}. modelsize parameter is not used.")
model_size_or_path = model_dir
elif modelsize is not None:
model_size_or_path = self.translate_model_name(modelsize)
logger.debug(f"Loading whisper model {modelsize}. You use mlx whisper, so {model_size_or_path} will be used.")
else:
raise ValueError("Either modelsize or model_dir must be set")
self.model_size_or_path = model_size_or_path
dtype = mx.float16
ModelHolder.get_model(model_size_or_path, dtype)
return transcribe
def translate_model_name(self, model_name):
model_mapping = {
"tiny.en": "mlx-community/whisper-tiny.en-mlx",
"tiny": "mlx-community/whisper-tiny-mlx",
"base.en": "mlx-community/whisper-base.en-mlx",
"base": "mlx-community/whisper-base-mlx",
"small.en": "mlx-community/whisper-small.en-mlx",
"small": "mlx-community/whisper-small-mlx",
"medium.en": "mlx-community/whisper-medium.en-mlx",
"medium": "mlx-community/whisper-medium-mlx",
"large-v1": "mlx-community/whisper-large-v1-mlx",
"large-v2": "mlx-community/whisper-large-v2-mlx",
"large-v3": "mlx-community/whisper-large-v3-mlx",
"large-v3-turbo": "mlx-community/whisper-large-v3-turbo",
"large": "mlx-community/whisper-large-mlx",
}
mlx_model_path = model_mapping.get(model_name)
if mlx_model_path:
return mlx_model_path
else:
raise ValueError(f"Model name '{model_name}' is not recognized or not supported.")
def transcribe(self, audio, init_prompt=""):
if self.transcribe_kargs:
logger.warning("Transcribe kwargs (vad, task) are not compatible with MLX Whisper and will be ignored.")
segments = self.model(
audio,
language=self.original_language,
initial_prompt=init_prompt,
word_timestamps=True,
condition_on_previous_text=True,
path_or_hf_repo=self.model_size_or_path,
)
return segments.get("segments", [])
def ts_words(self, segments) -> List[ASRToken]:
tokens = []
for segment in segments:
if segment.get("no_speech_prob", 0) > 0.9:
continue
for word in segment.get("words", []):
token = ASRToken(word["start"], word["end"], word["word"], probability=word["probability"])
tokens.append(token)
return tokens
def segments_end_ts(self, res) -> List[float]:
return [s["end"] for s in res]
def use_vad(self):
self.transcribe_kargs["vad_filter"] = True
def set_translate_task(self):
self.transcribe_kargs["task"] = "translate"
class OpenaiApiASR(ASRBase):
"""Uses OpenAI's Whisper API for transcription."""
def __init__(self, lan=None, temperature=0, logfile=sys.stderr):
self.logfile = logfile
self.modelname = "whisper-1"
self.original_language = None if lan == "auto" else lan
self.response_format = "verbose_json"
self.temperature = temperature
self.load_model()
self.use_vad_opt = False
self.task = "transcribe"
def load_model(self, *args, **kwargs):
from openai import OpenAI
self.client = OpenAI()
self.transcribed_seconds = 0
def ts_words(self, segments) -> List[ASRToken]:
"""
Converts OpenAI API response words into ASRToken objects while
optionally skipping words that fall into no-speech segments.
"""
no_speech_segments = []
if self.use_vad_opt:
for segment in segments.segments:
if segment.no_speech_prob > 0.8:
no_speech_segments.append((segment.start, segment.end))
tokens = []
for word in segments.words:
start = word.start
end = word.end
if any(s[0] <= start <= s[1] for s in no_speech_segments):
continue
tokens.append(ASRToken(start, end, word.word))
return tokens
def segments_end_ts(self, res) -> List[float]:
return [s.end for s in res.words]
def transcribe(self, audio_data, prompt=None, *args, **kwargs):
buffer = io.BytesIO()
buffer.name = "temp.wav"
sf.write(buffer, audio_data, samplerate=16000, format="WAV", subtype="PCM_16")
buffer.seek(0)
self.transcribed_seconds += math.ceil(len(audio_data) / 16000)
params = {
"model": self.modelname,
"file": buffer,
"response_format": self.response_format,
"temperature": self.temperature,
"timestamp_granularities": ["word", "segment"],
}
if self.task != "translate" and self.original_language:
params["language"] = self.original_language
if prompt:
params["prompt"] = prompt
proc = self.client.audio.translations if self.task == "translate" else self.client.audio.transcriptions
transcript = proc.create(**params)
logger.debug(f"OpenAI API processed accumulated {self.transcribed_seconds} seconds")
return transcript
def use_vad(self):
self.use_vad_opt = True
def set_translate_task(self):
self.task = "translate" |