Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -8,12 +8,12 @@ from TTS.api import TTS
|
|
8 |
|
9 |
class VoiceAssistant:
|
10 |
def __init__(self):
|
11 |
-
# Cargar
|
12 |
self.processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-xlsr-53-spanish")
|
13 |
self.model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-xlsr-53-spanish")
|
14 |
|
15 |
-
# Cargar
|
16 |
-
self.tts_model = TTS(model_name="tts_models/es/
|
17 |
|
18 |
# Par谩metros de audio
|
19 |
self.sample_rate = 16000
|
@@ -21,11 +21,11 @@ class VoiceAssistant:
|
|
21 |
self.p = pyaudio.PyAudio()
|
22 |
self.stream = self.p.open(format=pyaudio.paFloat32, channels=1, rate=self.sample_rate, input=True, frames_per_buffer=self.chunk_size)
|
23 |
|
24 |
-
# Palabras clave
|
25 |
self.keyword_activation = "jarvis"
|
26 |
self.keyword_deactivation = "detente"
|
27 |
|
28 |
-
# Estado de
|
29 |
self.listening = False
|
30 |
|
31 |
def vad_collector(self, vad_threshold=0.5):
|
@@ -34,12 +34,12 @@ class VoiceAssistant:
|
|
34 |
data = self.stream.read(self.chunk_size)
|
35 |
audio_chunk = np.frombuffer(data, dtype=np.float32)
|
36 |
|
37 |
-
# Detectar palabra
|
38 |
if self.keyword_activation.lower() in str(audio_chunk).lower():
|
39 |
keyword_detected = True
|
40 |
break
|
41 |
|
42 |
-
# Detectar palabra
|
43 |
if self.keyword_deactivation.lower() in str(audio_chunk).lower():
|
44 |
self.listening = False
|
45 |
break
|
@@ -51,12 +51,12 @@ class VoiceAssistant:
|
|
51 |
def transcribe_audio(self, audio_chunks):
|
52 |
audio_data = np.concatenate(audio_chunks)
|
53 |
|
54 |
-
#
|
55 |
input_values = self.processor(audio_data, return_tensors="pt", sampling_rate=self.sample_rate).input_values
|
56 |
with torch.no_grad():
|
57 |
logits = self.model(input_values).logits
|
58 |
|
59 |
-
#
|
60 |
predicted_ids = torch.argmax(logits, dim=-1)
|
61 |
transcription = self.processor.decode(predicted_ids[0])
|
62 |
|
@@ -73,19 +73,17 @@ class VoiceAssistant:
|
|
73 |
def run(self):
|
74 |
st.title("Asistente de Voz JARVIS")
|
75 |
|
|
|
76 |
if st.button("Iniciar/Detener Escucha"):
|
77 |
self.listening = not self.listening
|
78 |
-
if self.listening
|
79 |
-
|
80 |
-
|
81 |
-
st.write("Escucha desactivada.")
|
82 |
-
|
83 |
if self.listening:
|
84 |
audio_chunks, keyword_detected = self.vad_collector()
|
85 |
|
86 |
if keyword_detected:
|
87 |
st.success("Palabra clave 'JARVIS' detectada. Procesando...")
|
88 |
-
|
89 |
transcribed_text = self.transcribe_audio(audio_chunks)
|
90 |
st.write(f"Texto transcrito: {transcribed_text}")
|
91 |
|
|
|
8 |
|
9 |
class VoiceAssistant:
|
10 |
def __init__(self):
|
11 |
+
# Cargar modelo Wav2Vec2 para reconocimiento de voz en espa帽ol
|
12 |
self.processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-xlsr-53-spanish")
|
13 |
self.model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-xlsr-53-spanish")
|
14 |
|
15 |
+
# Cargar modelo TTS (Text-to-Speech) con modelo alternativo
|
16 |
+
self.tts_model = TTS(model_name="tts_models/es/tacotron2-DDC", progress_bar=False)
|
17 |
|
18 |
# Par谩metros de audio
|
19 |
self.sample_rate = 16000
|
|
|
21 |
self.p = pyaudio.PyAudio()
|
22 |
self.stream = self.p.open(format=pyaudio.paFloat32, channels=1, rate=self.sample_rate, input=True, frames_per_buffer=self.chunk_size)
|
23 |
|
24 |
+
# Palabras clave
|
25 |
self.keyword_activation = "jarvis"
|
26 |
self.keyword_deactivation = "detente"
|
27 |
|
28 |
+
# Estado de escucha
|
29 |
self.listening = False
|
30 |
|
31 |
def vad_collector(self, vad_threshold=0.5):
|
|
|
34 |
data = self.stream.read(self.chunk_size)
|
35 |
audio_chunk = np.frombuffer(data, dtype=np.float32)
|
36 |
|
37 |
+
# Detectar palabra de activaci贸n
|
38 |
if self.keyword_activation.lower() in str(audio_chunk).lower():
|
39 |
keyword_detected = True
|
40 |
break
|
41 |
|
42 |
+
# Detectar palabra de desactivaci贸n
|
43 |
if self.keyword_deactivation.lower() in str(audio_chunk).lower():
|
44 |
self.listening = False
|
45 |
break
|
|
|
51 |
def transcribe_audio(self, audio_chunks):
|
52 |
audio_data = np.concatenate(audio_chunks)
|
53 |
|
54 |
+
# Procesar y transcribir el audio usando Wav2Vec2
|
55 |
input_values = self.processor(audio_data, return_tensors="pt", sampling_rate=self.sample_rate).input_values
|
56 |
with torch.no_grad():
|
57 |
logits = self.model(input_values).logits
|
58 |
|
59 |
+
# Decodificar la transcripci贸n
|
60 |
predicted_ids = torch.argmax(logits, dim=-1)
|
61 |
transcription = self.processor.decode(predicted_ids[0])
|
62 |
|
|
|
73 |
def run(self):
|
74 |
st.title("Asistente de Voz JARVIS")
|
75 |
|
76 |
+
# Bot贸n para iniciar/desactivar la escucha
|
77 |
if st.button("Iniciar/Detener Escucha"):
|
78 |
self.listening = not self.listening
|
79 |
+
st.write("Escucha activada." if self.listening else "Escucha desactivada.")
|
80 |
+
|
81 |
+
# Realizar la transcripci贸n y s铆ntesis de voz si la escucha est谩 activada
|
|
|
|
|
82 |
if self.listening:
|
83 |
audio_chunks, keyword_detected = self.vad_collector()
|
84 |
|
85 |
if keyword_detected:
|
86 |
st.success("Palabra clave 'JARVIS' detectada. Procesando...")
|
|
|
87 |
transcribed_text = self.transcribe_audio(audio_chunks)
|
88 |
st.write(f"Texto transcrito: {transcribed_text}")
|
89 |
|