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from transformers import pipeline
import torch
from IPython.display import Audio
device = "cuda:0" if torch.cuda.is_available() else "cpu"
classifier = pipeline(
"audio-classification", model="MIT/ast-finetuned-speech-commands-v2", device=device
)
from transformers.pipelines.audio_utils import ffmpeg_microphone_live
print(classifier.model.config.id2label)
def launch_fn(
wake_word="marvin",
prob_threshold=0.5,
chunk_length_s=2.0,
stream_chunk_s=0.25,
debug=False,
):
if wake_word not in classifier.model.config.label2id.keys():
raise ValueError(
f"Wake word {wake_word} not in set of valid class labels, pick a wake word in the set {classifier.model.config.label2id.keys()}."
)
sampling_rate = classifier.feature_extractor.sampling_rate
mic = ffmpeg_microphone_live(
sampling_rate=sampling_rate,
chunk_length_s=chunk_length_s,
stream_chunk_s=stream_chunk_s,
)
print("Listening for wake word...")
for prediction in classifier(mic):
prediction = prediction[0]
if debug:
print(prediction)
if prediction["label"] == wake_word:
if prediction["score"] > prob_threshold:
return True
# launch_fn(debug=True)
transcriber = pipeline(
"automatic-speech-recognition", model="openai/whisper-base.en", device=device
)
import sys
def transcribe(chunk_length_s=5.0, stream_chunk_s=1.0):
sampling_rate = transcriber.feature_extractor.sampling_rate
mic = ffmpeg_microphone_live(
sampling_rate=sampling_rate,
chunk_length_s=chunk_length_s,
stream_chunk_s=stream_chunk_s,
)
print("Start speaking...")
for item in transcriber(mic, generate_kwargs={"max_new_tokens": 128}):
sys.stdout.write("\033[K")
print(item["text"], end="\r")
if not item["partial"][0]:
break
return item["text"]
from huggingface_hub import HfFolder
import requests
def query(text, model_id="tiiuae/falcon-7b-instruct"):
api_url = f"https://api-inference.huggingface.co/models/{model_id}"
headers = {"Authorization": f"Bearer {HfFolder().get_token()}"}
payload = {"inputs": text}
print(f"Querying...: {text}")
response = requests.post(api_url, headers=headers, json=payload)
return response.json()[0]["generated_text"][len(text) + 1 :]
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
from datasets import load_dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
def synthesise(text):
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(
inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder
)
return speech.cpu()
# launch_fn()
# print("hablá")
# transcription = transcribe()
# response = query(transcription)
# audio = synthesise(response)
audio = synthesise(
"Hugging Face is a company that provides natural language processing and machine learning tools for developers."
)
# import gradio as gr
# with gr.Blocks() as demo:
# boton = gr.Button("hablar")
# audio = gr.Audio()
# micro = gr.Microphone()
# boton.click(start,micro,audio)
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