Merge pull request #10 from SilasK/main

More flexibility by using custom tokenize_method  + black

silero_vad_iterator.py

@@ -6,15 +6,16 @@ import torch
 
 # Their licence is MIT, same as ours: https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/LICENSE
 
-class VADIterator:
-    def __init__(self,
-                 model,
-                 threshold: float = 0.5,
-                 sampling_rate: int = 16000,
-                 min_silence_duration_ms: int = 500,  # makes sense on one recording that I checked
-                 speech_pad_ms: int = 100             # same 
-                 ):
 
+class VADIterator:
+    def __init__(
+        self,
+        model,
+        threshold: float = 0.5,
+        sampling_rate: int = 16000,
+        min_silence_duration_ms: int = 500,  # makes sense on one recording that I checked
+        speech_pad_ms: int = 100,  # same
+    ):
         """
         Class for stream imitation
 
@@ -41,7 +42,9 @@ class VADIterator:
         self.sampling_rate = sampling_rate
 
         if sampling_rate not in [8000, 16000]:
-            raise ValueError('VADIterator does not support sampling rates other than [8000, 16000]')
+            raise ValueError(
+                "VADIterator does not support sampling rates other than [8000, 16000]"
+            )
 
         self.min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
         self.speech_pad_samples = sampling_rate * speech_pad_ms / 1000
@@ -80,7 +83,13 @@ class VADIterator:
         if (speech_prob >= self.threshold) and not self.triggered:
             self.triggered = True
             speech_start = self.current_sample - self.speech_pad_samples
-            return {'start': int(speech_start) if not return_seconds else round(speech_start / self.sampling_rate, 1)}
+            return {
+                "start": (
+                    int(speech_start)
+                    if not return_seconds
+                    else round(speech_start / self.sampling_rate, 1)
+                )
+            }
 
         if (speech_prob < self.threshold - 0.15) and self.triggered:
             if not self.temp_end:
@@ -91,26 +100,35 @@ class VADIterator:
                 speech_end = self.temp_end + self.speech_pad_samples
                 self.temp_end = 0
                 self.triggered = False
-                return {'end': int(speech_end) if not return_seconds else round(speech_end / self.sampling_rate, 1)}
+                return {
+                    "end": (
+                        int(speech_end)
+                        if not return_seconds
+                        else round(speech_end / self.sampling_rate, 1)
+                    )
+                }
 
         return None
 
+
 #######################
-# because Silero now requires exactly 512-sized audio chunks 
+# because Silero now requires exactly 512-sized audio chunks
 
 import numpy as np
+
+
 class FixedVADIterator(VADIterator):
-    '''It fixes VADIterator by allowing to process any audio length, not only exactly 512 frames at once.
-    If audio to be processed at once is long and multiple voiced segments detected, 
-    then __call__ returns the start of the first segment, and end (or middle, which means no end) of the last segment. 
-    '''
+    """It fixes VADIterator by allowing to process any audio length, not only exactly 512 frames at once.
+    If audio to be processed at once is long and multiple voiced segments detected,
+    then __call__ returns the start of the first segment, and end (or middle, which means no end) of the last segment.
+    """
 
     def reset_states(self):
         super().reset_states()
-        self.buffer = np.array([],dtype=np.float32)
+        self.buffer = np.array([], dtype=np.float32)
 
     def __call__(self, x, return_seconds=False):
-        self.buffer = np.append(self.buffer, x) 
+        self.buffer = np.append(self.buffer, x)
         ret = None
         while len(self.buffer) >= 512:
             r = super().__call__(self.buffer[:512], return_seconds=return_seconds)
@@ -118,29 +136,28 @@ class FixedVADIterator(VADIterator):
             if ret is None:
                 ret = r
             elif r is not None:
-                if 'end' in r:
-                    ret['end'] = r['end']  # the latter end
-                if 'start' in r and 'end' in ret:  # there is an earlier start.
+                if "end" in r:
+                    ret["end"] = r["end"]  # the latter end
+                if "start" in r and "end" in ret:  # there is an earlier start.
                     # Remove end, merging this segment with the previous one.
-                    del ret['end']
+                    del ret["end"]
         return ret if ret != {} else None
 
+
 if __name__ == "__main__":
     # test/demonstrate the need for FixedVADIterator:
 
     import torch
-    model, _ = torch.hub.load(
-        repo_or_dir='snakers4/silero-vad',
-        model='silero_vad'
-    )
+
+    model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad")
     vac = FixedVADIterator(model)
-#   vac = VADIterator(model)  # the second case crashes with this
+    #   vac = VADIterator(model)  # the second case crashes with this
 
     # this works: for both
-    audio_buffer = np.array([0]*(512),dtype=np.float32)
+    audio_buffer = np.array([0] * (512), dtype=np.float32)
     vac(audio_buffer)
 
-    # this crashes on the non FixedVADIterator with 
+    # this crashes on the non FixedVADIterator with
     # ops.prim.RaiseException("Input audio chunk is too short", "builtins.ValueError")
-    audio_buffer = np.array([0]*(512-1),dtype=np.float32)
+    audio_buffer = np.array([0] * (512 - 1), dtype=np.float32)
     vac(audio_buffer)

whisper_fastapi_online_server.py

@@ -22,10 +22,21 @@ app.add_middleware(
 
 
 parser = argparse.ArgumentParser(description="Whisper FastAPI Online Server")
-parser.add_argument("--host", type=str, default='localhost', help="The host address to bind the server to.")
-parser.add_argument("--port", type=int, default=8000, help="The port number to bind the server to.")
-parser.add_argument("--warmup-file", type=str, dest="warmup_file", 
-        help="The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast. It can be e.g. https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav .")
+parser.add_argument(
+    "--host",
+    type=str,
+    default="localhost",
+    help="The host address to bind the server to.",
+)
+parser.add_argument(
+    "--port", type=int, default=8000, help="The port number to bind the server to."
+)
+parser.add_argument(
+    "--warmup-file",
+    type=str,
+    dest="warmup_file",
+    help="The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast. It can be e.g. https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav .",
+)
 add_shared_args(parser)
 args = parser.parse_args()
 
@@ -35,29 +46,38 @@ asr, online = asr_factory(args)
 with open("src/live_transcription.html", "r") as f:
     html = f.read()
 
+
 @app.get("/")
 async def get():
     return HTMLResponse(html)
 
+
 SAMPLE_RATE = 16000
 CHANNELS = 1
 SAMPLES_PER_SEC = SAMPLE_RATE * int(args.min_chunk_size)
-BYTES_PER_SAMPLE = 2               # s16le = 2 bytes per sample
+BYTES_PER_SAMPLE = 2  # s16le = 2 bytes per sample
 BYTES_PER_SEC = SAMPLES_PER_SEC * BYTES_PER_SAMPLE
 
+
 async def start_ffmpeg_decoder():
     """
     Start an FFmpeg process in async streaming mode that reads WebM from stdin
     and outputs raw s16le PCM on stdout. Returns the process object.
     """
     process = (
-        ffmpeg
-        .input('pipe:0', format='webm')
-        .output('pipe:1', format='s16le', acodec='pcm_s16le', ac=CHANNELS, ar=str(SAMPLE_RATE))
+        ffmpeg.input("pipe:0", format="webm")
+        .output(
+            "pipe:1",
+            format="s16le",
+            acodec="pcm_s16le",
+            ac=CHANNELS,
+            ar=str(SAMPLE_RATE),
+        )
         .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True)
     )
     return process
 
+
 @app.websocket("/asr")
 async def websocket_endpoint(websocket: WebSocket):
     await websocket.accept()
@@ -65,6 +85,7 @@ async def websocket_endpoint(websocket: WebSocket):
 
     ffmpeg_process = await start_ffmpeg_decoder()
     pcm_buffer = bytearray()
+
     # Continuously read decoded PCM from ffmpeg stdout in a background task
     async def ffmpeg_stdout_reader():
         nonlocal pcm_buffer
@@ -75,10 +96,16 @@ async def websocket_endpoint(websocket: WebSocket):
             try:
                 elapsed_time = int(time() - beg)
                 beg = time()
-                chunk = await loop.run_in_executor(None, ffmpeg_process.stdout.read, 32000*elapsed_time)
-                if not chunk: # The first chunk will be almost empty, FFmpeg is still starting up
-                    chunk = await loop.run_in_executor(None, ffmpeg_process.stdout.read, 4096)
-                    if not chunk: # FFmpeg might have closed
+                chunk = await loop.run_in_executor(
+                    None, ffmpeg_process.stdout.read, 32000 * elapsed_time
+                )
+                if (
+                    not chunk
+                ):  # The first chunk will be almost empty, FFmpeg is still starting up
+                    chunk = await loop.run_in_executor(
+                        None, ffmpeg_process.stdout.read, 4096
+                    )
+                    if not chunk:  # FFmpeg might have closed
                         print("FFmpeg stdout closed.")
                         break
 
@@ -86,21 +113,29 @@ async def websocket_endpoint(websocket: WebSocket):
 
                 if len(pcm_buffer) >= BYTES_PER_SEC:
                     # Convert int16 -> float32
-                    pcm_array = np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32) / 32768.0
+                    pcm_array = (
+                        np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32)
+                        / 32768.0
+                    )
                     pcm_buffer = bytearray()
                     online.insert_audio_chunk(pcm_array)
                     transcription = online.process_iter()[2]
                     full_transcription += transcription
                     if args.vac:
-                        buffer = online.online.to_flush(online.online.transcript_buffer.buffer)[2] # We need to access the underlying online object to get the buffer
+                        buffer = online.online.to_flush(
+                            online.online.transcript_buffer.buffer
+                        )[
+                            2
+                        ]  # We need to access the underlying online object to get the buffer
                     else:
                         buffer = online.to_flush(online.transcript_buffer.buffer)[2]
-                    if buffer in full_transcription: # With VAC, the buffer is not updated until the next chunk is processed
+                    if (
+                        buffer in full_transcription
+                    ):  # With VAC, the buffer is not updated until the next chunk is processed
                         buffer = ""
-                    await websocket.send_json({
-                        "transcription": transcription,
-                        "buffer": buffer
-                    })
+                    await websocket.send_json(
+                        {"transcription": transcription, "buffer": buffer}
+                    )
             except Exception as e:
                 print(f"Exception in ffmpeg_stdout_reader: {e}")
                 break
@@ -135,8 +170,11 @@ async def websocket_endpoint(websocket: WebSocket):
             pass
 
         ffmpeg_process.wait()
-        
-        
+
+
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run("whisper_fastapi_online_server:app", host=args.host, port=args.port, reload=True)
+
+    uvicorn.run(
+        "whisper_fastapi_online_server:app", host=args.host, port=args.port, reload=True
+    )