setup.py

@@ -25,7 +25,7 @@ whisper_clib = ('whisper_clib', {'sources': ['whisper.cpp/ggml.c']})
 
 setup(
     name='whispercpp',
-    version='1.2.0',
+    version='1.1.0',
     description='Python bindings for whisper.cpp - ecker edition',
     author='lightmare',
     author_email='',

whispercpp.pxd

@@ -102,7 +102,6 @@ cdef extern from "whisper.h" nogil:
 	cdef int whisper_full(whisper_context*, whisper_full_params, float*, int)
 	cdef int whisper_full_parallel(whisper_context*, whisper_full_params, float*, int, int)
 	cdef int whisper_full_n_segments(whisper_context*)
-	cdef int whisper_full_lang_id(whisper_context*)
 	cdef int64_t whisper_full_get_segment_t0(whisper_context*, int)
 	cdef int64_t whisper_full_get_segment_t1(whisper_context*, int)
 	# Unknown CtypesSpecial name='c_char_p'

whispercpp.pyx

@@ -92,49 +92,8 @@ cdef cnp.ndarray[cnp.float32_t, ndim=1, mode="c"] load_audio(bytes file, int sr
 				capture_stderr=True
 			)
 		)[0]
-	except ffmpeg.Error as e:
+	except Exception:
-		raise RuntimeError(f"failed to load audio: {e.stderr.decode('utf8')}") from e
+		raise RuntimeError(f"File '{file}' not found")
-
-	cdef cnp.ndarray[cnp.float32_t, ndim=1, mode="c"] frames = (
-		np.frombuffer(out, np.int16)
-		.flatten()
-		.astype(np.float32)
-	) / pow(2, 15)
-
-	return frames
-
-cdef cnp.ndarray[cnp.float32_t, ndim=1, mode="c"] load_audio_segment(bytes file, float start = 0, float end = 0, int sr = SAMPLE_RATE): # TODO: merge load_audio and load_audio_segment
-	if start >= end:
-		raise ValueError("start must be less than end")
-
-	# check if start and end values fit the input
-	probe = ffmpeg.probe(file, select_streams='a', ac=1) # TODO: let user specify stream index?
-
-	audio_stream = probe['streams'][0]
-	stream_start = float(audio_stream['start_time'])
-	stream_end = stream_start + float(probe['format']['duration'])
-
-	if stream_start > start or stream_end < end:
-		raise ValueError(f"start value {start} and end value {end} do not make sense with given file's audio stream: {stream_start} to {stream_end}")
-
-	try:
-		out, _ = (
-			ffmpeg.input(file, threads=0)
-			.filter('atrim', start=start, end=end)
-			.filter('asetpts', 'PTS-STARTPTS')
-			.output(
-				"-", format="s16le",
-				acodec="pcm_s16le",
-				ac=1, ar=sr
-			)
-			.run(
-				cmd=["ffmpeg", "-nostdin"],
-				capture_stdout=True,
-				capture_stderr=True
-			)
-		)
-	except ffmpeg.Error as e:
-		raise RuntimeError(f"failed to load audio: {e.stderr.decode('utf8')}") from e
 
 	cdef cnp.ndarray[cnp.float32_t, ndim=1, mode="c"] frames = (
 		np.frombuffer(out, np.int16)
@@ -161,19 +120,7 @@ cdef class Whisper:
 	cdef whisper_context * ctx
 	cdef whisper_full_params params
 
-	def __init__(
+	def __init__(self, model = DEFAULT_MODEL, models_dir = MODELS_DIR, _Bool print_realtime = PRINT_REALTIME, _Bool print_progress = PRINT_PROGRESS, _Bool token_timestamps = TOKEN_TIMESTAMPS, _Bool print_timestamps = PRINT_TIMESTAMPS, _Bool translate = TRANSLATE, char* language = LANGUAGE, int n_threads = N_THREADS, _Bool print_system_info = False): # not pretty, look for a way to use kwargs?
-			self,
-			model = DEFAULT_MODEL,
-			models_dir = MODELS_DIR,
-			_Bool print_realtime = PRINT_REALTIME,
-			_Bool print_progress = PRINT_PROGRESS,
-			_Bool token_timestamps = TOKEN_TIMESTAMPS,
-			_Bool print_timestamps = PRINT_TIMESTAMPS,
-			_Bool translate = TRANSLATE,
-			char* language = LANGUAGE,
-			int n_threads = N_THREADS,
-			_Bool print_system_info = False
-		): # not pretty, look for a way to use kwargs?
 		"""Constructor for Whisper class.
 
 		Automatically checks for model and downloads it if necessary.
@@ -209,7 +156,7 @@ cdef class Whisper:
 		    filename: Path to file
 
 		Returns:
-		    Return value of whisper_full for extract_*(...)
+		    Return value of whisper_full for extract_text(...)
 
 		Raises:
 		    RuntimeError: The given file could not be found
@@ -220,27 +167,7 @@ cdef class Whisper:
 
 		#print("Transcribing..")
 		return whisper_full(self.ctx, self.params, &frames[0], len(frames))
-
+	
-	def transcribe_segment(self, filename, start, end):
-		"""Transcribes a segment from `start` to `end` from a given file.
-
-		Args:
-		    filename: Path to file
-		    start: Start time
-		    end : End time
-
-		Returns:
-		    Return value of whisper_full for extract_*(...)
-
-		Raises:
-		    RuntimeError: The given file could not be found
-		    ValueError: The given timestamps do not fit the audio stream
-		"""
-		if start >= end:
-			raise ValueError("start must be less than end")
-		cdef cnp.ndarray[cnp.float32_t, ndim=1, mode="c"] frames = load_audio_segment(<bytes>filename, start, end)
-		return whisper_full(self.ctx, self.params, &frames[0], len(frames))
-
 	def extract_text(self, int res):
 		"""Extracts the text from a transcription.
 
@@ -261,52 +188,6 @@ cdef class Whisper:
 			whisper_full_get_segment_text(self.ctx, i).decode() for i in range(n_segments)
 		]
 
-	def extract_segment(self, int res, _Bool resolve_lang = True):
-		"""Extracts the text from a transcription.
-
-		Args:
-		    res: A return value from transcribe(...)
-		    resolve_lang: Whether the language param `language` should be resolved to the auto-detected value
-
-		Returns:
-		    A dict of the following format:
-		    {
-		        'text': '',
-		        'start': 0.0,
-		        'end': 20.0,
-		        'segments': [
-		            {
-		                'text': '',
-		                'start': 0.0,
-		                'end': 20.0
-		            }, ...
-		        ],
-		        'language': 'en'
-		    }
-
-		Raises:
-		    RuntimeError: The given return value was invalid.
-		"""
-		if res != 0:
-			raise RuntimeError
-
-		cdef int n_segments = whisper_full_n_segments(self.ctx)
-		segments = []
-		for i in range(n_segments):
-			text = whisper_full_get_segment_text(self.ctx, i).decode()
-			start = float(whisper_full_get_segment_t0(self.ctx, i)) / 100
-			end = float(whisper_full_get_segment_t1(self.ctx, i)) / 100
-			segments.append({ 'text': text, 'start': start, 'end': end })
-		text = ''.join([s['text'] for s in segments])
-		start = segments[0]['start']
-		end = segments[-1]['end']
-		language = <bytes>self.params.language
-
-		if resolve_lang and language == b'auto':
-			language = whisper_lang_str(whisper_full_lang_id(self.ctx))
-
-		return { 'text': text, 'start': start, 'end': end, 'segments': segments, 'language': language.decode() }
-
 	def extract_text_and_timestamps(self, int res):
 		"""Extracts the text and timestamps from a transcription.