remove mic test and streams
This commit is contained in:
parent
f3907703ed
commit
2ec2266929
3 changed files with 0 additions and 248 deletions
|
|
@ -1,95 +0,0 @@
|
||||||
from microphone_stream import MicrophoneStream
|
|
||||||
from voice_activity_controller import VoiceActivityController
|
|
||||||
from whisper_online import *
|
|
||||||
import numpy as np
|
|
||||||
import librosa
|
|
||||||
import io
|
|
||||||
import soundfile
|
|
||||||
import sys
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class SimpleASRProcessor:
|
|
||||||
|
|
||||||
def __init__(self, asr, sampling_rate = 16000):
|
|
||||||
"""run this when starting or restarting processing"""
|
|
||||||
self.audio_buffer = np.array([],dtype=np.float32)
|
|
||||||
self.prompt_buffer = ""
|
|
||||||
self.asr = asr
|
|
||||||
self.sampling_rate = sampling_rate
|
|
||||||
self.init_prompt = ''
|
|
||||||
|
|
||||||
def ts_words(self, segments):
|
|
||||||
result = ""
|
|
||||||
for segment in segments:
|
|
||||||
if segment.no_speech_prob > 0.9:
|
|
||||||
continue
|
|
||||||
for word in segment.words:
|
|
||||||
w = word.word
|
|
||||||
t = (word.start, word.end, w)
|
|
||||||
result +=w
|
|
||||||
return result
|
|
||||||
|
|
||||||
def stream_process(self, vad_result):
|
|
||||||
iter_in_phrase = 0
|
|
||||||
for chunk, is_final in vad_result:
|
|
||||||
iter_in_phrase += 1
|
|
||||||
|
|
||||||
if chunk is not None:
|
|
||||||
sf = soundfile.SoundFile(io.BytesIO(chunk), channels=1,endian="LITTLE",samplerate=SAMPLING_RATE, subtype="PCM_16",format="RAW")
|
|
||||||
audio, _ = librosa.load(sf,sr=SAMPLING_RATE)
|
|
||||||
out = []
|
|
||||||
out.append(audio)
|
|
||||||
a = np.concatenate(out)
|
|
||||||
self.audio_buffer = np.append(self.audio_buffer, a)
|
|
||||||
|
|
||||||
if is_final and len(self.audio_buffer) > 0:
|
|
||||||
res = self.asr.transcribe(self.audio_buffer, init_prompt=self.init_prompt)
|
|
||||||
tsw = self.ts_words(res)
|
|
||||||
|
|
||||||
self.init_prompt = self.init_prompt + tsw
|
|
||||||
self.init_prompt = self.init_prompt [-100:]
|
|
||||||
self.audio_buffer.resize(0)
|
|
||||||
iter_in_phrase =0
|
|
||||||
|
|
||||||
yield True, tsw
|
|
||||||
# show progress evry 50 chunks
|
|
||||||
elif iter_in_phrase % 50 == 0 and len(self.audio_buffer) > 0:
|
|
||||||
res = self.asr.transcribe(self.audio_buffer, init_prompt=self.init_prompt)
|
|
||||||
# use custom ts_words
|
|
||||||
tsw = self.ts_words(res)
|
|
||||||
yield False, tsw
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
SAMPLING_RATE = 16000
|
|
||||||
|
|
||||||
model = "large-v2"
|
|
||||||
src_lan = "en" # source language
|
|
||||||
tgt_lan = "en" # target language -- same as source for ASR, "en" if translate task is used
|
|
||||||
use_vad = False
|
|
||||||
min_sample_length = 1 * SAMPLING_RATE
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
vac = VoiceActivityController(use_vad_result = use_vad)
|
|
||||||
asr = FasterWhisperASR(src_lan, "large-v2") # loads and wraps Whisper model
|
|
||||||
|
|
||||||
tokenizer = create_tokenizer(tgt_lan)
|
|
||||||
online = SimpleASRProcessor(asr)
|
|
||||||
|
|
||||||
|
|
||||||
stream = MicrophoneStream()
|
|
||||||
stream = vac.detect_user_speech(stream, audio_in_int16 = False)
|
|
||||||
stream = online.stream_process(stream)
|
|
||||||
|
|
||||||
for isFinal, text in stream:
|
|
||||||
if isFinal:
|
|
||||||
print( text, end="\r\n")
|
|
||||||
else:
|
|
||||||
print( text, end="\r")
|
|
||||||
|
|
@ -1,71 +0,0 @@
|
||||||
from microphone_stream import MicrophoneStream
|
|
||||||
from voice_activity_controller import VoiceActivityController
|
|
||||||
from whisper_online import *
|
|
||||||
import numpy as np
|
|
||||||
import librosa
|
|
||||||
import io
|
|
||||||
import soundfile
|
|
||||||
import sys
|
|
||||||
|
|
||||||
|
|
||||||
SAMPLING_RATE = 16000
|
|
||||||
model = "large-v2"
|
|
||||||
src_lan = "en" # source language
|
|
||||||
tgt_lan = "en" # target language -- same as source for ASR, "en" if translate task is used
|
|
||||||
use_vad_result = True
|
|
||||||
min_sample_length = 1 * SAMPLING_RATE
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
asr = FasterWhisperASR(src_lan, model) # loads and wraps Whisper model
|
|
||||||
tokenizer = create_tokenizer(tgt_lan) # sentence segmenter for the target language
|
|
||||||
online = OnlineASRProcessor(asr, tokenizer) # create processing object
|
|
||||||
|
|
||||||
microphone_stream = MicrophoneStream()
|
|
||||||
vad = VoiceActivityController(use_vad_result = use_vad_result)
|
|
||||||
|
|
||||||
complete_text = ''
|
|
||||||
final_processing_pending = False
|
|
||||||
out = []
|
|
||||||
out_len = 0
|
|
||||||
for iter in vad.detect_user_speech(microphone_stream): # processing loop:
|
|
||||||
raw_bytes= iter[0]
|
|
||||||
is_final = iter[1]
|
|
||||||
|
|
||||||
if raw_bytes:
|
|
||||||
sf = soundfile.SoundFile(io.BytesIO(raw_bytes), channels=1,endian="LITTLE",samplerate=SAMPLING_RATE, subtype="PCM_16",format="RAW")
|
|
||||||
audio, _ = librosa.load(sf,sr=SAMPLING_RATE)
|
|
||||||
out.append(audio)
|
|
||||||
out_len += len(audio)
|
|
||||||
|
|
||||||
|
|
||||||
if (is_final or out_len >= min_sample_length) and out_len>0:
|
|
||||||
a = np.concatenate(out)
|
|
||||||
online.insert_audio_chunk(a)
|
|
||||||
|
|
||||||
if out_len > min_sample_length:
|
|
||||||
o = online.process_iter()
|
|
||||||
print('-----'*10)
|
|
||||||
complete_text = complete_text + o[2]
|
|
||||||
print('PARTIAL - '+ complete_text) # do something with current partial output
|
|
||||||
print('-----'*10)
|
|
||||||
out = []
|
|
||||||
out_len = 0
|
|
||||||
|
|
||||||
if is_final:
|
|
||||||
o = online.finish()
|
|
||||||
# final_processing_pending = False
|
|
||||||
print('-----'*10)
|
|
||||||
complete_text = complete_text + o[2]
|
|
||||||
print('FINAL - '+ complete_text) # do something with current partial output
|
|
||||||
print('-----'*10)
|
|
||||||
online.init()
|
|
||||||
out = []
|
|
||||||
out_len = 0
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -1,82 +0,0 @@
|
||||||
|
|
||||||
|
|
||||||
### mic stream
|
|
||||||
|
|
||||||
import queue
|
|
||||||
import re
|
|
||||||
import sys
|
|
||||||
import pyaudio
|
|
||||||
|
|
||||||
|
|
||||||
class MicrophoneStream:
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
sample_rate: int = 16000,
|
|
||||||
):
|
|
||||||
"""
|
|
||||||
Creates a stream of audio from the microphone.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
chunk_size: The size of each chunk of audio to read from the microphone.
|
|
||||||
channels: The number of channels to record audio from.
|
|
||||||
sample_rate: The sample rate to record audio at.
|
|
||||||
"""
|
|
||||||
try:
|
|
||||||
import pyaudio
|
|
||||||
except ImportError:
|
|
||||||
raise Exception('py audio not installed')
|
|
||||||
|
|
||||||
self._pyaudio = pyaudio.PyAudio()
|
|
||||||
self.sample_rate = sample_rate
|
|
||||||
|
|
||||||
self._chunk_size = int(self.sample_rate * 40 / 1000)
|
|
||||||
self._stream = self._pyaudio.open(
|
|
||||||
format=pyaudio.paInt16,
|
|
||||||
channels=1,
|
|
||||||
rate=sample_rate,
|
|
||||||
input=True,
|
|
||||||
frames_per_buffer=self._chunk_size,
|
|
||||||
)
|
|
||||||
|
|
||||||
self._open = True
|
|
||||||
|
|
||||||
def __iter__(self):
|
|
||||||
"""
|
|
||||||
Returns the iterator object.
|
|
||||||
"""
|
|
||||||
|
|
||||||
return self
|
|
||||||
|
|
||||||
def __next__(self):
|
|
||||||
"""
|
|
||||||
Reads a chunk of audio from the microphone.
|
|
||||||
"""
|
|
||||||
if not self._open:
|
|
||||||
raise StopIteration
|
|
||||||
|
|
||||||
try:
|
|
||||||
return self._stream.read(self._chunk_size)
|
|
||||||
except KeyboardInterrupt:
|
|
||||||
raise StopIteration
|
|
||||||
|
|
||||||
def close(self):
|
|
||||||
"""
|
|
||||||
Closes the stream.
|
|
||||||
"""
|
|
||||||
|
|
||||||
self._open = False
|
|
||||||
|
|
||||||
if self._stream.is_active():
|
|
||||||
self._stream.stop_stream()
|
|
||||||
|
|
||||||
self._stream.close()
|
|
||||||
self._pyaudio.terminate()
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Loading…
Reference in a new issue