diff --git a/tests/test_transcribe.py b/tests/test_transcribe.py
index 599221af5..e68a8ba49 100644
--- a/tests/test_transcribe.py
+++ b/tests/test_transcribe.py
@@ -7,18 +7,37 @@
 from whisper.tokenizer import get_tokenizer
 
 
+class TestingProgressReceiver(whisper.TranscribeProgressReceiver):
+    def start(self, total: int):
+        self.result = ""
+        self.total = total
+        self.progress = 0
+        return self
+    def update_line(self, start: float, end: float, text: str):
+        self.result += text
+    def update(self, n):
+        self.progress += n
+    def get_result(self):
+        return self.result
+    def verify_total(self):
+        return self.total == self.progress
+
 @pytest.mark.parametrize("model_name", whisper.available_models())
 def test_transcribe(model_name: str):
     device = "cuda" if torch.cuda.is_available() else "cpu"
     model = whisper.load_model(model_name).to(device)
     audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
+    receiver = TestingProgressReceiver()
 
     language = "en" if model_name.endswith(".en") else None
     result = model.transcribe(
-        audio_path, language=language, temperature=0.0, word_timestamps=True
+        audio_path, language=language, temperature=0.0, word_timestamps=True,
+        progress_receiver=receiver
     )
+    assert receiver.verify_total()
     assert result["language"] == "en"
     assert result["text"] == "".join([s["text"] for s in result["segments"]])
+    assert result["text"] == receiver.get_result()
 
     transcription = result["text"].lower()
     assert "my fellow americans" in transcription
diff --git a/whisper/__init__.py b/whisper/__init__.py
index e210718f3..5546ab5bd 100644
--- a/whisper/__init__.py
+++ b/whisper/__init__.py
@@ -11,7 +11,7 @@
 from .audio import load_audio, log_mel_spectrogram, pad_or_trim
 from .decoding import DecodingOptions, DecodingResult, decode, detect_language
 from .model import ModelDimensions, Whisper
-from .transcribe import transcribe
+from .transcribe import TranscribeProgressReceiver, transcribe
 from .version import __version__
 
 _MODELS = {
diff --git a/whisper/transcribe.py b/whisper/transcribe.py
index 8e1240bd6..1ed5bc256 100644
--- a/whisper/transcribe.py
+++ b/whisper/transcribe.py
@@ -2,7 +2,7 @@
 import os
 import traceback
 import warnings
-from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union, Self
 
 import numpy as np
 import torch
@@ -34,12 +34,57 @@
 if TYPE_CHECKING:
     from .model import Whisper
 
+class TranscribeProgressReceiver:
+    """
+    A class that allows external classes to inherit and handle transcription progress in customized
+    manners.
+    """
+    def start(self, total: int) -> Self:
+        """
+        The method is called when the transcription starts with integral `total` parameter in frames.
+        In most case this method should return `self`
+        """
+        return self
+    def update(self, n: int):
+        """
+        The `update` method is called with increment `n` in frames whenever a segment is transcribed.
+        """
+        pass
+    def update_line(self, start: float, end: float, text: str):
+        """
+        It is called whenever a segment is transcribed.
+
+        Parameters
+        ----------
+        start: float
+            The floating point start time of the segment in seconds
+
+        end: float
+            The floating point end time of the segment in seconds
+
+        text: str
+            The transcribed text
+        """
+        pass
+    def __enter__(self) -> Self:
+        """
+        Inherit this method if resources allocation is needed at the start of the transcription.
+        In most cases this method should return `self`
+        """
+        return self
+    def __exit__(self, exception_type, exception_value, exception_traceback):
+        """
+        Inherit this method if resources need to be released when the transcription is finished or
+        terminated.
+        """
+        pass
 
 def transcribe(
     model: "Whisper",
     audio: Union[str, np.ndarray, torch.Tensor],
     *,
     verbose: Optional[bool] = None,
+    progress_receiver: TranscribeProgressReceiver = TranscribeProgressReceiver(),
     temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
     compression_ratio_threshold: Optional[float] = 2.4,
     logprob_threshold: Optional[float] = -1.0,
@@ -253,7 +298,8 @@ def new_segment(
     # show the progress bar when verbose is False (if True, transcribed text will be printed)
     with tqdm.tqdm(
         total=content_frames, unit="frames", disable=verbose is not False
-    ) as pbar:
+    ) as pbar, \
+    progress_receiver.start(total=content_frames) as ext_progress:
         last_speech_timestamp = 0.0
         # NOTE: This loop is obscurely flattened to make the diff readable.
         # A later commit should turn this into a simpler nested loop.
@@ -459,10 +505,11 @@ def next_words_segment(segments: List[dict]) -> Optional[dict]:
                 if last_word_end is not None:
                     last_speech_timestamp = last_word_end
 
-            if verbose:
-                for segment in current_segments:
-                    start, end, text = segment["start"], segment["end"], segment["text"]
-                    line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
+            for segment in current_segments:
+                start, end, text = segment["start"], segment["end"], segment["text"]
+                line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
+                ext_progress.update_line(start, end, make_safe(text))
+                if verbose:
                     print(make_safe(line))
 
             # if a segment is instantaneous or does not contain text, clear it
@@ -490,6 +537,7 @@ def next_words_segment(segments: List[dict]) -> Optional[dict]:
 
             # update progress bar
             pbar.update(min(content_frames, seek) - previous_seek)
+            ext_progress.update(min(content_frames, seek) - previous_seek)
 
     return dict(
         text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),