format code

* Clarify that the returned segments value is a generator

* Update README.md

.gitignore

@@ -1 +1,15 @@
+# Byte-compiled / Optimized / DLL Files
 *.pyc
+*.pyo
+*.pyd
+__pycache__/
+
+# Distribution / Packaging
+venv/
+
+# Unit Test
+.pytest_cache/
+
+# Ignore IDE, Editor Files
+.idea/
+.vscode/

README.md

@@ -87,6 +87,13 @@ for segment in segments:
     print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
 ```
 
+**Warning:** `segments` is a *generator* so the transcription only starts when you iterate over it. The transcription can be run to completion by gathering the segments in a list or a `for` loop:
+
+```python
+segments, _ = model.transcribe("audio.mp3")
+segments = list(segments)  # The transcription will actually run here.
+```
+
 #### Word-level timestamps
 
 ```python

faster_whisper/tokenizer.py

@@ -125,19 +125,21 @@ class Tokenizer:
             current_tokens.append(token)
             decoded = self.decode_with_timestamps(current_tokens)
 
-            if (
-                replacement_char not in decoded
-                or decoded_full[unicode_offset + decoded.index(replacement_char)]
-                == replacement_char
+            try:
+                replacement_char_index = decoded.index(replacement_char)
+                replacement_char_index += unicode_offset
+            except ValueError:
+                replacement_char_index = None
+
+            if replacement_char_index is None or (
+                replacement_char_index < len(decoded_full)
+                and decoded_full[replacement_char_index] == replacement_char
             ):
                 words.append(decoded)
                 word_tokens.append(current_tokens)
                 current_tokens = []
                 unicode_offset += len(decoded)
 
-                if unicode_offset >= len(decoded_full):
-                    break
-
         return words, word_tokens
 
     def split_tokens_on_spaces(

faster_whisper/transcribe.py

@@ -1,4 +1,5 @@
 import itertools
+import logging
 import os
 import zlib
 
@@ -11,7 +12,7 @@ import tokenizers
 from faster_whisper.audio import decode_audio
 from faster_whisper.feature_extractor import FeatureExtractor
 from faster_whisper.tokenizer import Tokenizer
-from faster_whisper.utils import download_model
+from faster_whisper.utils import download_model, format_timestamp, get_logger
 from faster_whisper.vad import (
     SpeechTimestampsMap,
     collect_chunks,
@@ -71,6 +72,7 @@ class WhisperModel:
         compute_type: str = "default",
         cpu_threads: int = 0,
         num_workers: int = 1,
+        download_root: Optional[str] = None,
     ):
         """Initializes the Whisper model.
 
@@ -92,11 +94,15 @@ class WhisperModel:
             having multiple workers enables true parallelism when running the model
             (concurrent calls to self.model.generate() will run in parallel).
             This can improve the global throughput at the cost of increased memory usage.
+          download_root: Directory where the model should be saved. If not set, the model
+            is saved in the standard Hugging Face cache directory.
         """
+        self.logger = get_logger()
+
         if os.path.isdir(model_size_or_path):
             model_path = model_size_or_path
         else:
-            model_path = download_model(model_size_or_path)
+            model_path = download_model(model_size_or_path, download_root)
 
         self.model = ctranslate2.models.Whisper(
             model_path,
@@ -211,17 +217,40 @@ class WhisperModel:
             - a generator over transcribed segments
             - an instance of AudioInfo
         """
-        if not isinstance(audio, np.ndarray):
-            audio = decode_audio(
-                audio, sampling_rate=self.feature_extractor.sampling_rate
-            )
+        sampling_rate = self.feature_extractor.sampling_rate
 
-        duration = audio.shape[0] / self.feature_extractor.sampling_rate
+        if not isinstance(audio, np.ndarray):
+            audio = decode_audio(audio, sampling_rate=sampling_rate)
+
+        duration = audio.shape[0] / sampling_rate
+
+        self.logger.info(
+            "Processing audio with duration %s", format_timestamp(duration)
+        )
 
         if vad_filter:
             vad_parameters = {} if vad_parameters is None else vad_parameters
             speech_chunks = get_speech_timestamps(audio, **vad_parameters)
             audio = collect_chunks(audio, speech_chunks)
+
+            self.logger.info(
+                "VAD filter removed %s of audio",
+                format_timestamp(duration - (audio.shape[0] / sampling_rate)),
+            )
+
+            if self.logger.isEnabledFor(logging.DEBUG):
+                self.logger.debug(
+                    "VAD filter kept the following audio segments: %s",
+                    ", ".join(
+                        "[%s -> %s]"
+                        % (
+                            format_timestamp(chunk["start"] / sampling_rate),
+                            format_timestamp(chunk["end"] / sampling_rate),
+                        )
+                        for chunk in speech_chunks
+                    ),
+                )
+
         else:
             speech_chunks = None
 
@@ -239,6 +268,12 @@ class WhisperModel:
                 results = self.model.detect_language(encoder_output)
                 language_token, language_probability = results[0][0]
                 language = language_token[2:-2]
+
+                self.logger.info(
+                    "Detected language '%s' with probability %.2f",
+                    language,
+                    language_probability,
+                )
         else:
             language_probability = 1
 
@@ -275,9 +310,7 @@ class WhisperModel:
         segments = self.generate_segments(features, tokenizer, options, encoder_output)
 
         if speech_chunks:
-            segments = restore_speech_timestamps(
-                segments, speech_chunks, self.feature_extractor.sampling_rate
-            )
+            segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate)
 
         audio_info = AudioInfo(
             language=language,
@@ -297,6 +330,7 @@ class WhisperModel:
         content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames
         seek = 0
         all_tokens = []
+        all_prompt_text = []
         prompt_reset_since = 0
 
         if options.initial_prompt is not None:
@@ -312,6 +346,11 @@ class WhisperModel:
             )
             segment_duration = segment_size * self.feature_extractor.time_per_frame
 
+            if self.logger.isEnabledFor(logging.DEBUG):
+                self.logger.debug(
+                    "Processing segment at %s", format_timestamp(time_offset)
+                )
+
             previous_tokens = all_tokens[prompt_reset_since:]
             prompt = self.get_prompt(
                 tokenizer,
@@ -339,6 +378,12 @@ class WhisperModel:
                     should_skip = False
 
                 if should_skip:
+                    self.logger.debug(
+                        "No speech threshold is met (%f > %f)",
+                        result.no_speech_prob,
+                        options.no_speech_threshold,
+                    )
+
                     # fast-forward to the next segment boundary
                     seek += segment_size
                     continue
@@ -457,7 +502,15 @@ class WhisperModel:
                 if segment["start"] == segment["end"] or not text.strip():
                     continue
 
-                all_tokens.extend(tokens)
+                check_prompt_num = 1
+                if all(
+                    [
+                        text.strip() != i.strip()
+                        for i in all_prompt_text[-check_prompt_num:]
+                    ]
+                ):
+                    all_tokens.extend(tokens)
+                    all_prompt_text.append(text)
 
                 yield Segment(
                     start=segment["start"],
@@ -543,12 +596,26 @@ class WhisperModel:
             ):
                 needs_fallback = True  # too repetitive
 
+                self.logger.debug(
+                    "Compression ratio threshold is not met with temperature %.1f (%f > %f)",
+                    temperature,
+                    compression_ratio,
+                    options.compression_ratio_threshold,
+                )
+
             if (
                 options.log_prob_threshold is not None
                 and avg_log_prob < options.log_prob_threshold
             ):
                 needs_fallback = True  # average log probability is too low
 
+                self.logger.debug(
+                    "Log probability threshold is not met with temperature %.1f (%f < %f)",
+                    temperature,
+                    avg_log_prob,
+                    options.log_prob_threshold,
+                )
+
             if not needs_fallback:
                 break
 
@@ -721,14 +788,18 @@ def restore_speech_timestamps(
                     end=ts_map.get_original_time(word.end, chunk_index),
                 )
                 words.append(word)
-        else:
-            words = segment.words
 
-        segment = segment._replace(
-            start=ts_map.get_original_time(segment.start),
-            end=ts_map.get_original_time(segment.end),
-            words=words,
-        )
+            segment = segment._replace(
+                start=words[0].start,
+                end=words[-1].end,
+                words=words,
+            )
+
+        else:
+            segment = segment._replace(
+                start=ts_map.get_original_time(segment.start),
+                end=ts_map.get_original_time(segment.end),
+            )
 
         yield segment
 

faster_whisper/utils.py

@@ -1,3 +1,4 @@
+import logging
 import os
 
 from typing import Optional
@@ -25,6 +26,11 @@ def get_assets_path():
     return os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets")
 
 
+def get_logger():
+    """Returns the module logger."""
+    return logging.getLogger("faster_whisper")
+
+
 def download_model(size: str, output_dir: Optional[str] = None):
     """Downloads a CTranslate2 Whisper model from the Hugging Face Hub.
 

setup.py

@@ -23,7 +23,7 @@ conversion_requires = get_requirements(
 
 setup(
     name="faster-whisper",
-    version="0.4.0",
+    version="0.4.1",
     license="MIT",
     description="Faster Whisper transcription with CTranslate2",
     long_description=get_long_description(),